Design And In-Silico Evaluation of Pyridine-4-Carbohydrazide Derivatives for Potential Therapeutic Applications

Research Article | DOI: https://doi.org/10.31579/2690-1897/235

Design And In-Silico Evaluation of Pyridine-4-Carbohydrazide Derivatives for Potential Therapeutic Applications

  • Sofian S. Mohamed 1
  • Salah M Bensaber 2
  • Nisreen H Meiqal 2
  • Anton Hermann 3
  • Abdul M Gbaj 2*

1Department of Chemistry and Toxins, Judicial Expertise and Research Center, Tripoli, Libya.

2Department of Medicinal Chemistry, Faculty of Pharmacy, University of Tripoli, Tripoli, Libya.

3Department of Biosciences, University of Salzburg, Salzburg, Austria.

*Corresponding Author: Abdul M Gbaj, Professor of Genetics and Biochemistry, Department of Medicinal Chemistry,Faculty of Pharmacy, University of Tripoli, Tripoli, Libya.

Citation: Sofian S. Mohamed, Salah M Bensaber, Nisreen H Meiqal, Anton Hermann, Abdul M Gbaj. (2025). Design And In-Silico Evaluation of Pyridine-4-Carbohydrazide Derivatives for Potential Therapeutic Applications, J, Surgical Case Reports and Images, 8(3); DOI:10.31579/2690-1897/235

Copyright: © 2025, Abdul M Gbaj. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: 10 February 2025 | Accepted: 26 February 2025 | Published: 05 March 2025

Keywords: drug discovery; drug repositioning; in-silico study; adme properties

Abstract

The emergence of drug-resistant and novel diseases underscores the urgency for innovative therapeutic interventions. Drug repositioning and computational approaches offer an efficient pathway to accelerate drug discovery and development. This study leverages these techniques in designing and evaluating derivatives based on the FDA-approved compound, pyridine-4-carbohydrazide, to assess how structural modifications impact therapeutic potential.

Methods: The derivatives were designed using a chemical library of small molecules containing imine functional groups, built upon pyridine-4-carbohydrazide scaffolds (INH01-INH19). Computational tools, including Molinspiration Cheminformatics, way2drug, and the pkCSM platform, were utilized to evaluate each derivative's physicochemical properties, drug-likeness, bioactivity scores, potential biological activities, and ADME (Absorption, Distribution, Metabolism, Excretion) profiles.

Results: Most derivatives demonstrated enhanced physicochemical characteristics, adhering to both Lipinski’s Rule of Five and Veber’s Rule. Bioactivity scores varied with moderate to inactive interactions across six target classes, ranked as follows: enzyme inhibitors, kinase inhibitors, G-protein-coupled receptors, protease inhibitors, nuclear receptors, and ion channel modulators. Notably, derivatives INH03, INH09, INH14, and INH19 exhibited high predicted activity in multiple therapeutic areas, indicating potential applications in antibacterial, antiviral, antiprotozoal, anti-inflammatory and anticancer treatments. Moreover, structural modifications in these derivatives positively influenced ADME profiles compared to pyridine-4-carbohydrazide, though certain compounds presented challenges, such as limited solubility, P-glycoprotein interactions and CYP450 inhibition. 

Conclusions: These Schiff base derivatives stand out as promising candidates for further drug development, underscoring the importance of computational strategies in optimizing drug discovery and design.

1 | Introduction

A drug, in the context of medicine, is a chemical or biological substance designed to interact with biological processes within an organism to treat, diagnose, or prevent disease (Benedetti, 2014). These agents can originate from diverse sources, including natural, semisynthetic, or synthetic origins(Pollock et al., 2024). The continuous need for new medications arises from several factors, including the toxicity and side effects of existing drugs(Kroschinsky et al., 2017), the emergence of new diseases(Khan et al., 2020), the development of drug resistance(Jackson et al., 2018), and advancements in our understanding of health conditions(Subbiah, 2023). These challenges actuate pharmaceutical research and development to innovate, improve treatment efficacy and address previously unmet medical needs. The traditional process of drug discovery follows a well-established procedure, beginning with the identification and validation of molecular targets(Salazar & Gormley, 2017). This is typically followed by high-throughput screening of extensive chemical libraries to identify potential lead compounds(Sinha &Vohora, 2017). Conventional drug discovery methods heavily rely on empirical approaches, involving extensive in vitro and in vivo testing to evaluate a compound's efficacy, pharmacokinetics and toxicity. Although this approach has led to the development of numerous successful therapies, it is both time-consuming and costly, often requiring over a decade and more than $2.5 billion to successfully bring a new drug to market(Kumari et al., 2022; Schlander et al., 2021). To overcome these challenges, alternative approaches such as drug repurposing, also known as drug repositioning, have gained prominence(Ahmad et al., 2021; Talevi& Bellera, 2020a). Drug repositioning involves identifying new therapeutic uses for existing drugs, whether FDA-approved, withdrawn, or outdated. This approach leverages the well-established safety profiles of known drugs, allowing for faster and less costly drug development(Gazerani, 2019). Drug repositioning can also involve using an existing drug as a template for synthesizing new analogs that exhibit activity against other diseases(Cha et al., 2018). This strategy has distinct advantages over conventional drug discovery, including shorter development timelines, reduced investment needs, and higher success rates(Pushpakom et al., 2018; Wu et al., 2019). Approximately 33% of drugs approved in recent years have resulted from drug repositioning efforts, underscoring its effectiveness as a modern drug discovery strategy(Talevi& Bellera, 2020b).

Advances in computational technologies, bioinformatics, and proteomics have significantly accelerated the drug discovery process through the integration of in-silico methods(Liao et al., 2022). Computational approaches, commonly known as computer-aided drug design (CADD), have become indispensable tools at every stage of drug development(Kapetanovic, 2008). CADD enables the transformation of biological target information into computational models, allowing for data computation, analysis, and the prediction of compound activities. Molecular docking, virtual screening, and machine learning help filter large chemical libraries into smaller, more promising subsets for experimental validation. Moreover, CADD offers critical insights for lead compound optimization, focusing on improving binding affinity, pharmacokinetics, and toxicity profiles, as well as designing novel compounds via structural modifications(Sliwoski et al., 2014). The benefits of CADD are vast, providing substantial reductions in the time, cost, and experimental scope traditionally required for drug discovery. By predicting and optimizing compound properties in silico, CADD can shorten the research timeline and reduce development costs by up to 50%(Sachin S Padole et al., 2022; Xiang et al., 2012). Additionally, as computational accuracy continues to improve, these predictions increasingly align with experimental outcomes, bolstering the credibility and reliability of in silico approaches. Today, CADD is widely employed in the search for treatments for a range of diseases, including cancer(Chua et al., 2023; Reddy et al., 2007), Diabetes(Balamurugan et al., 2012; Semighini et al., 2011), and infectious diseases caused by viruses (Chen et al., 1994; Doyon et al., 2005; Yang et al., 2024)and bacteria(Duan et al., 2019; Njogu et al., 2016; Supuran, 2017).

Pyridine-4-carbohydrazide, commonly known as isoniazid, has served as a cornerstone in the treatment of tuberculosis. Its efficacy stems from its ability to inhibit the synthesis of mycolic acids, essential components of the mycobacterial cell wall(Vilchèze&Jacobs, 2019). The chemical structure of isoniazid, as depicted in Figure 1, presents a versatile molecular framework for the development of agents with a broad spectrum of biological activities.One of the common approaches isreplacing the hydrazide hydrogen with different functional groups can alter the molecule's polarity, hydrophobicity, hydrogen bonding capacity, and overall molecular conformation(Mali et al., 2021; Raczuk et al., 2022). This versatility has led to the synthesis of a plethora of derivatives with expanded therapeutic applications such as anti-inflammatory(Zhang et al., 2020), antitubercular(Aboui-Fadl et al., 2003), anticancer(Firmino et al., 2016; Rodrigues et al., 2014), antimicrobial and urease inhibitory activity(Habala et al., 2016), antidepressant and analgesic properties(Uddin et al., 2020), in the treatment of Alzheimer’s disease(Santos et al., 2020). Additionally, the pyridine moiety itself plays a crucial role in enhancing drug permeability, biochemical potency, and metabolic stability. Moreover, the pyridine ring's ability to form various non-covalent interactions with protein targets facilitates drug-target binding(Ling et al., 2021; Pennington & Moustakas, 2017). The FDA database provides compelling evidence of the prevalence of pyridine-based drugs. Approximately 18% of approved heterocyclic drugs incorporate pyridine or its derivatives, highlighting its significance as a structural motif in medicinal chemistry(Ling et al., 2021).

                                                                                       Figure 1: Chemicalstructure of Pyridine-4-Carbohydrazide (Isoniazid, INH).

Based on these advancements, this study aims to design and evaluate a chemical library of small molecules incorporating imine functional groups and pyridine-4-carbohydrazide scaffolds. Leveraging computational methods, we will predict the physicochemical properties, drug-likeness, bioactivity profiles, and ADME characteristics of these derivatives before synthesis and screening. By doing so, we aim to reduce resource waste and avoid unnecessary time and expenses associated with screening compounds that have a low likelihood of activity. This proactive approach is intended to streamline the identification of promising agents, enhancing the efficiency of the drug discovery pipeline and reducing the risk of failure in later stages.

Materials and Methods

Design Strategy

In this study, compounds (Schiff bases) were designed by utilizing the FDA-approved drug pyridine-4-carbohydrazide(A) and hybridized with substituted aldehydes (B). The imine group (C), formed during this process, was used to augment the lipophilic behavior part, as illustrated in Scheme 1. The R groups vary in type, position, partition coefficient, and hydrogen bonding capacity. This modification was intended to investigate the influence of various substituents on the phenyl ring and their impact on the biological activity of these compounds.A total of nineteen compounds were designed and assigned the code INH01-INH19.

Scheme 1: The Design Concept for Schiff Base Compounds 

                (probably use large letter C in the figure!)

In silicoStudy

This study was conducted using a HP computer system with the following specifications: Windows 11 operating system, Intel® Core™ i5-1135G7 Quad-core Processor @ 2.40GHz, and 8 Gigabytes of RAM. The chemical structures of nineteen compounds (INH01-INH19) were initially generated using the ChemDrawsoftware suite as in Table 1A. The in-silico workflow commenced with the preparation of molecular structures for the designed compounds. These structures were first constructed in the two-dimensional (2D) format using Chem3D Ultra software and saved in the Structure Data File (SDF) format. The 2D molecular representations were then converted into SMILES (Simplified Molecular Input Line Entry System) format using the online SMILES Translator tool provided by the National Cancer Institute (https://cactus.nci.nih.gov/translate/). To ensure structural accuracy, the resulting SMILES strings were meticulously validated against their original chemical structures, as detailed in Table 1B. Following validation, the SMILES data served as the key input for subsequent predictive analysis. The data was uploaded to various online platforms, enabling the calculation of essential molecular properties and the prediction of potential biological activities for the designed compounds. This computational workflow provided valuable insights into the physicochemical, drug-likeness, bioactivity, and pharmacokinetic characteristics of the pyridine-4-carbohydrazide derivatives under investigation. The specific online platforms employed and the details of the obtained predictions are elaborated upon in subsequent sections.

Physicochemical, Drug-likeness, and bioactivity Properties predictions

The physicochemical properties, drug-likeness, and bioactivity of the compounds were evaluated using the molinspirationchemoinformatics platform. Drug-likeness assessments were conducted according to Lipinski's Rule of Five and Veber's rules. Additionally, the platform predicted bioactivity against six different protein targets: GPCR, ICM, KI, NRC, PI, and EI.

Prediction of Activity Spectra for Substances (PASS)

To further elucidate the biological activity spectrum of the pyridine-4-carbohydrazide derivatives, the PASS online tool (https://www.way2drug.com/passonline/) was employed.

ADME Prediction

The ADME properties of the derivatives were predicted using the pkCSM-Pharmacokinetics software (https://biosig.lab.uq.edu.au/pkcsm/). Established methodologies documented in the scientific literature served as the foundation for these predictions.

Table 1A: Codes and chemical Structures of the designed compounds.

Table 1B: IUPAC names and canonical SMILES of the predicted compounds.

CodesCharacter 
INH01IUPAC nameN'-[phenylmethylidene] pyridine-4-carbohydrazide
SMILESO=C(NN=Cc1ccccc1)c2ccncc2
INH02IUPAC nameN-(2-hydroxybenzylidene) pyridine-4-carbohydrazide
SMILESO=C(NN=Cc1ccccc1O)c2ccncc2
INH03IUPAC nameN'-[(3-nitrophenyl) methylidene] pyridine-4-carbohydrazide
SMILESC1=CC(=CC(=C1)[N+](=O)[O-])C=NNC(=O)C2=CC=NC=C2
INH04IUPAC nameN`-[(Z)-(4-Chlorophenyl) methylidene] pyridine-4-carbohydrazide
SMILESClC1=CC=C(\C=N\NC(=O)C2=CC=NC=C2)C=C1
INH05IUPAC nameN-[(4-Hydroxy-3-methoxybenzylidene) pyridine-4-carbohydrazide
SMILESCOC1=C(C=CC(=C1)C=NNC(=O)C2=CC=NC=C2)O
INH06IUPAC name(N'-(3-ethoxy-4-hydroxybenzylidene) pyridine-4-carbohydrazide
SMILESCCOc2cc(C=NNC(=O)c1ccncc1)ccc2O
INH07IUPAC nameN-(4-(dimethylamino) benzylidene) pyridine-4-carbohydrazide
SMILESCN(C)C1=CC=C(C=C1)C=NNC(=O)C2=CC=NC=C2
INH08IUPAC name(E)-N'-(4-methoxybenzylidene) pyridine-4-carbohydrazide
SMILESCOc2ccc(C=NNC(=O)c1ccncc1)cc2
INH09IUPAC name(E)-N'-((2-hydroxynaphthalen-1-yl) methylene) pyridine-4-carbohydrazide
SMILESO=C(NN=Cc1c(O)ccc2ccccc12)c3ccncc3
INH10IUPAC nameN'-(2-methoxy-4-nitrobenzylidene) pyridine-4-carbohydrazide
SMILESCOc1cc(N(=O)=O)ccc1C=NNC(=O)c2ccncc2
INH11IUPAC nameN'-(2-hydroxy-5-methoxybenzylidene) pyridine-4-carbohydrazide
SMILESCOc2ccc(O)c(C=NNC(=O)c1ccncc1)c2
INH12IUPAC nameN'-(4-nitrobenzylidene) pyridine-4-carbohydrazide
SMILESO=C(NN=Cc1ccc(N(=O)=O)cc1)c2ccncc2
INH13IUPAC nameN'-(furan-2-ylmethylene)pyridine-4-carbohydrazide
SMILESO=C(NN=Cc1ccco1)c2ccncc2
INH14IUPAC nameN'-((4-(dimethylamino) naphthalen-1-yl) methylene) pyridine-4-carbohydrazide
SMILESCN(C)c2ccc(C=NNC(=O)c1ccncc1)c3ccccc23
INH15IUPAC nameN-(4-methylbenzylidene) pyridine-4-carbohydrazide
SMILESCC1=CC=C(C=C1)C=NNC(=O)C2=CC=NC=C2
INH16IUPAC nameN'-[thiophen-2-yl] pyridine-4-carbohydrazide
SMILESC1=CSC(=C1)/C=N\NC(=O)C2=CC=NC=C2
INH17IUPAC nameN'-[(2-nitrophenyl) methylidene] pyridine-4-carbohydrazide
SMILESC1=CC=C(C(=C1)/C=N/NC(=O)C2=CC=NC=C2)[N+](=O)[O-]
INH18IUPAC nameN-[(E)-[(Z)-3-phenylprop-2-enylidene]amino] pyridine-4-carbohydrazide
SMILESC1=CC=C(C=C1)/C=C\C=N\NC(=O)C2=CC=NC=C2
INH19IUPAC nameN'-[(E)-(naphthalen-2-yl) methylidene] pyridine-4-carbohydrazide
SMILESO=C(NN=Cc1cccc2ccccc12)c3ccncc3
INHIUPAC namePyridine-4-carbohydrazide
SMILESC1=CN=CC=C1C(=O)NN

Table 1C: Distribution of predictors used in the in-Silico Study.

ParameterPredictor  UnitRequirement value
Physiochemical and drug-likeness properties 

 

Lipinski's

Rule

 

Molecular weight (MW)g/mol<500>
Partition coefficient (LogP)-< 5>
Number of hydrogen bond acceptors -<10>
Number of hydrogen bond donors -< 5>

Veber

Rule

Number of rotatable bonds (N-rotb)-< 5>
Topological polar surface area (TPSA)-<140>
Bioactivity score properties 
BioactivityscoreG-protein-coupledreceptor ligands (GPCR)-
  • Compounds with a bioactivity score greater than 0.00 are considered active.

 

  • Compounds with a bioactivity score ranging from -0.50 to 0.00 are moderately active.

 

  • Compounds with a bioactivity score less than -0.50 are deemed inactive.
Ion channel modulation (ICM)-
Kinase inhibitor (KI)-
Nuclear receptor ligands (NRL)-
Protease inhibitor (PI)-
Enzyme inhibitor (EI)-
Pharmacokinetic properties 
AbsorptionWater solubilityLogS-
Intestinal absorption (HIA)

Results and discussion

Design strategy

This study presents an approach to the repositioning of isoniazid by introducing structural modifications at the terminal at the terminal NH2 group. The strategy involves hybridizing isoniazid with various aromatic or heteroaromatic aldehydes,leading to the formation of a new functional group known as an imine. The selection of aromatic aldehydes as hybridization partners was motivated by their structural features. Compared to chains, aromatic aldehydes exhibit fewer degrees of freedom, which can contribute to enhanced ligand-receptor binding energy by reducing the entropic penalty. This property can potentially lead to increased compound potency(Mushtaque & Rizvi, 2023; Rohilla et al., 2024).

The designed compounds possess several key features:

  1. Nucleophilic imine and reactive nitrogen: The imine group and the nitrogen in the pyridine ring offer nucleophilic sites for potential interactions with biological targets.
  2. Electrophilic and nucleophilic character of the imine carbon: The imine carbon's dual reactivity can facilitate interactions with both electron-rich and electron-deficient groups.
  3. Tautomerism potential: A carbonyl group adjacent to the -NH- group in hydrazine allows for the possibility of tautomerism in certain cases.
  4. Intramolecular and intermolecular interactions: The relative positioning of the NH group to the -C=N group can influence the propensity for intramolecular and intermolecular interactions, potentially enhancing binding to biological targets.
  5. Smaller molecular weight: The reduced molecular weight of these derivatives compared to isoniazid may facilitate intracellular penetration, mimic endogenous substrates, and increase the likelihood of interactions with various targets.

The structural modifications introduced in these compounds are expected to significantly influence their physicochemical properties, including lipophilicity, electronic characteristics, and steric effects. These alterations may, in turn, lead to changes in biological activity and therapeutic potential. Ultimately, the goal of these modifications is to improve the compounds' efficacy, particularly for applications as DNA-binding agents.

Physiochemical and drug-likeness properties predictions

Prediction of the physicochemical properties of drugcandidates is essential for efficient drug development and understanding their biological and medicinal actions(Leeson & Young, 2015; Meanwell, 2011).  Properties such as molecular weight, the number of rotatable bonds, and the number of heavy atoms are integral to evaluating drug-likeness, helping identify oral drug candidates in the early phases of drug discovery(Lee et al., 2022a; Tripathi &Ayyannan, 2018).Drug-like compounds are molecules that contain functional groups and/or have physical properties consistent with the majority of known drugs, suggesting that these compounds could potentially exhibit biological activity or therapeutic effects. The drug-like characteristics serve as a parameter in choosing a more promising compound as a lead from the extensive combinatorial libraries(Lee et al., 2022b; Tian et al., 2015).

One of the foundational methods for assessing drug-like properties is Lipinski’s Rule of Five (Ro5), developed by Pfizer's medicinal chemist, Christopher Lipinski. The RO5 was derived from an analysis of orally available drugs and clinical candidates, though it excludes certain classes such as antibiotics, antifungals, vitamins, and cardiac glycosides. The RO5 states that a compound is more likely to be membrane permeable and easily absorbed via passive diffusion in human intestine if it meets the following criteria: molecular weight (MW) <500>

Molinspiration web-based software plays a pivotal role in evaluating physicochemical properties and drug-likeness. This tool leverages advanced Bayesian statistical methods, integrating the structural and property data of both active and inactive compounds to identify substructural features characteristic of biologically active molecules. The software calculates key physicochemical parameters crucial for predicting the theoretical oral bioavailability of the compounds under investigation. These parameters include molecular weight, partition coefficient (logP), the number of hydrogen bond acceptors and donors, the number of rotatable bonds, and total polar surface area, as outlined in Table 4A.

Number of heavy atoms (N atoms):

The number of heavy atoms in a molecule is a key factor in drug design, influencing molecular size, complexity, drug-likeness, and pharmacokinetic properties(García-Sosa et al., 2012). While larger molecules offer increased binding potential, they may also pose challenges related to synthesis, solubility, and membrane permeability. During lead optimization, reducing the number of heavy atoms can lower molecular weight and improve solubility, enhancing the drug-likeness profile without significantly affecting potency(de Souza Neto et al., 2020; Wang et al., 2019). Isoniazid derivatives contain between 16 and 24 heavy atoms, with INH having the lowest count at 10 atoms. The increase in heavy atoms in derivatives is often due to the substitution of larger aromatic rings or additional functional groups, such as in INH14, which has the highest count due to the inclusion of a naphthalene ring and a dimethylamino group.

Molecular weight (MW):

Molecular weightis the sum of the atomic weights of all atoms in a molecule, typically expressed in Daltons (Da) or grams per mole (g/mol). MW is a critical determinant of a drug’s absorption, distribution, metabolism, and excretion (ADME) profile(Komura et al., 2023). As MW increases, drug permeability and absorption generally decrease, particularly concerning membrane permeability and penetration through the blood-brain barrier (BBB)(Pardridge, 2012). Additionally, drug clearance through artificial membranes inversely correlates with MW(Lienx& Wang, 1980). The MW values of the designed compounds range from 215.21 to 318.38 Da, all of which are under the 500 Da threshold, suggesting that these molecules are likely to be easily absorbed and exhibit good permeability across cell membranes.

Partition coefficient (LogP):

The partition coefficient is a key measure of lipophilicity or hydrophobicity, calculated as the logarithm of the concentration ratio of a compound between organic (usually n-octanol) and aqueous phases(Ruiz-Garcia et al., 2008). Positive values of the partition coefficient suggest a tendency towards a lipophilic or hydrophobic environment,whereas negative values indicate a preference for a lipophobic or hydrophilic environment(Khanna & Ranganathan, 2009). LogP values significantly impact various ADMET parameters, drug-receptor interactions, and the overall potency of molecules(Tshepelevitsh et al., 2020). Compounds exhibiting extremely high or low LogP values may encounter challenges related to permeability and solubility(Waring, 2010). Highly hydrophilic compounds generally struggle to diffuse passively through cellular membranes due to their inability to penetrate the hydrophobic core of the lipid bilayer. Conversely, excessively lipophilic compounds may also face difficulties in membrane permeation, as they tend to become sequestered within the bilayer, impeding their effective transit(Lagorce et al., 2017).Researchers have also identified a correlation between a compound's logP value and its ability to penetrate the blood-brain barrier (BBB), a crucial factor for central nervous system (CNS) activity. For CNS-active drugs, a logP value in the range of 4 to 5 is generally considered optimal(Abraham et al., 1993; Feher et al., 2000). The LogP values for the designed compounds (INH01–INH19) were within the acceptable range per Lipinski’s Rule and showed superior lipophilicity compared to the precursor compound, isoniazid (INH), which had a negative LogP value. This enhancement in lipophilicity, likely attributed to the presence of the Imine group and aromatic portion of the aldehyde, suggests enhanced absorption through biological membranes.

Hydrogen bond acceptor and hydrogen bond donor groups:

Hydrogen bonds play a crucial role in molecular recognition(Morozov &Kortemme, 2005; Santos-Martins & Forli, 2020), structural stability(Pace et al., 2011), enzyme catalysis(Calixto et al., 2019; Neves et al., 2017), and drug partition and permeability(Rezai et al., 2006; Shinoda, 2016). The presence of functional groups capable of forming hydrogen bonds can enhance a drug's solubility and its ability to interact with biomolecular targets, thereby influencing binding affinity and selectivity. However, an excess of hydrogen bond donors or acceptors can negatively impact membrane permeability and partitioning(Alex et al., 2011). In that regard, drug-like character predictors, such as Lipinski's rule of five (Ro5) have been using the number of hydrogen bond donors/acceptors as a molecular descriptor.In the designed compounds, the number of HBAs ranges from 4 to 8, and the number of HBDs ranges from 1 to 2, all within the limits set by the Ro5, ensuring that the hydrogen bonding potential does not compromise the compounds' drug-like properties.

The number of rotatable bonds (N rotb):

The number of rotatable bonds is a measure of molecular flexibility and is an important descriptor for predicting oral bioavailability.  A rotatable bond is defined as any single bond not part of a ring and bound to a non-terminal heavy atom(Veber et al., 2002). A higher number of rotatable bonds increases molecular flexibility, potentially improving binding affinity with target proteins. However, compounds with fewer rotatable bonds are generally more rigid, which can enhance oral bioavailability by reducing the entropy cost of binding(Vieth et al., 2004). In the designed compounds, the number of rotatable bonds does not exceed 10, which is favorable for maintaining good oral bioavailability.

Topological polar surface area (TPSA): 

Topological polar surface area is a key descriptor related to hydrogen bonding and is important for drug transport properties such as intestinal absorption, BBB penetration, and oral bioavailability(Leeson, 2016; Veber et al., 2002). TPSA is calculated as the sum of the surface areas of polar atoms, primarily oxygen, and nitrogen, including attached hydrogens(Ertl et al., 2000). Therefore, TPSA is a reliable indicator of a compound's hydrogen bonding capacity. Compounds with a TPSA below 140 Ų typically exhibit good permeability and oral absorption, while those with a TPSA below 80 Ų are more likely to permeate the CNS by passive diffusion(Clark, 2011). The TPSA values for the isoniazid derivatives range from 54.59 to 109.14 Ų, well below the 140 Ų threshold, suggesting favorable absorption characteristics.

Molecular volume (Å3):

Molecular volume, a fundamental property of a molecule, plays a pivotal role in drug discovery. Its significance extends to optimizing drug candidates, understanding interactions with biological targets, and predicting pharmacokinetic properties(Flatow et al., 2014). By determining the molecular volume of a compound, researchers can gain insights into its binding affinity, solubility, permeability, and overall drug-likeness(La-Scalea et al., 2005; Mcgowan, 1956). In the context of isoniazid derivatives, molecular volume variations offer valuable information. The range of molecular volumes observed in these derivatives, from 187.51 ų to 295.84 ų, highlights the influence of structural modifications on the overall molecular size and shape. This understanding can inform the design of novel derivatives with improved properties.

Bioactivity predictions

In drug discovery, predicting the bioactivity of compounds against specific biological targets is crucial for understanding their potential therapeutic effects and toxicity profiles. Biological targets, which commonly include proteins,whether cytosolic, or membrane-embedded,and nucleic acids, play a key role in mediating the biological activity of drugs(Decherchi& Cavalli, 2020). To assess the likelihood of compounds interacting with these targets, bioactivity scores can be calculated using tools like Molinspiration, an open-source chemoinformatics platform. These scores provide valuable insights into compounds' binding affinity and selectivity, facilitating the development of new drugs with enhanced efficacy and reduced side effects.Bioactivity scores are categorized as follows: compounds with scores greater than 0.0 are likely to exhibit significant biological activity, scores between -0.50 and 0.00 suggest moderate activity, and scores below -0.50 indicate inactivity(Khan et al., 2017). Table 4B suggests the compounds exhibit moderate to inactive interactions with six protein targets. The efficiency of bioactivity scores typically follows the order of Enzyme Inhibitors (EI), Kinase Inhibitors (KI), G-protein-coupled receptors (GPCR), Protease Inhibitors (PI), Nuclear Receptors (NRC), and Ion Channel Modulators (ICM). The bioactivity profiles of isoniazid derivatives (INH01 to INH19) vary significantly depending on the structural modifications made to the pyridine-4-carbohydrazide core. For instance, the parent compound, isoniazid, shows relatively low bioactivity scores across all six targets, particularly in the categories of Nuclear Receptors (NRC: -2.33) and Ion Channel Modulators (ICM: -1.45). This suggests a low predicted affinity for these targets, consistent with isoniazid's primary role as an antibiotic rather than a modulator of these pathways. Derivatives such as INH01 (Phenylmethylidene) exhibit moderate activity across all targets, indicating that simple phenyl substitution does not dramatically alter activity compared to the parent compound. In contrast, derivatives like INH13 (Furan-2-yl) and INH16 (Thiophen-2-yl), which feature heterocyclic aromatic rings, display the lowest activity scores, implying that these substitutions may lead to reduced bioactivity. The presence of sulfur in the thiophene ring of INH16 could contribute to an electron-rich environment that is less favorable for target interactions. Compounds with strong electron-withdrawing groups, such as INH03 (3-Nitrophenyl) and INH17 (2-Nitrophenyl), show decreased bioactivity across all targets, particularly for ICM and NRC. This suggests that the electron-withdrawing nature of the nitro group reduces binding affinity. Conversely, electron-donating groups like methoxy and hydroxy in derivatives such as INH05 (4-Hydroxy-3-methoxyphenyl) and INH11 (2-Hydroxy-5-methoxyphenyl) slightly improve bioactivity, possibly by enhancing electronic interactions with certain targets. Derivatives with dimethylamino groups, such as INH07 (4-Dimethylaminobenzylidene) and INH14 (4-(Dimethylamino)naphthalen-1-yl), show moderate activity, indicating that electron-donating groups can enhance bioactivity, though the overall effect also depends on the position and presence of additional substituents. Meanwhile, bulky naphthalene-containing derivatives like INH09 (2-Hydroxynaphthalen-1-yl) and INH19 (Naphthalen-2-yl) generally exhibit low bioactivity, likely due to increased steric hindrance that reduces effective target binding. Lastly, INH18 ((Z)-3-phenylprop-2-enylidene) shows moderate bioactivity, suggesting that some flexibility in molecular structure can slightly improve target binding, though not significantly.

PASS prediction

The Prediction of Activity Spectra for Substances (PASS) platform provides a robust computational approach for forecasting the biological activity spectra of chemical compounds. Developed by the V. N. Orechovich Institute of Biomedical Chemistry, PASS predicts pharmacological effects based on structural similarities to known biologically active compounds. This model relies on a vast dataset, primarily derived from the MDL Drug Data Report (MDDR), and continuously updated to reflect discoveries in medicinal chemistry(Parasuraman, 2011).

The interpretation of PASS predictions requires a degree of flexibility, particularly concerning the values of Pa. A Pa value greater than 0.7 indicates a strong probability that the predicted biological activity can be experimentally confirmed. This suggests that the compound shares significant structural similarities with known pharmacologically active agents, making it a promising candidate for experimental validation. For Pa values between 0.5 and 0.7, the probability of experimental confirmation is lower. However, such compounds may exhibit novel structural features that are not closely aligned with known drugs. These novel features could offer valuable insights into unique or less common mechanisms of action. In contrast, a P value below 0.5 suggests a relatively low probability of experimental validation. Nonetheless, compounds with low Pa values may still exhibit structural novelty, presenting opportunities for discovery in previously unexplored areas of biological activity(Filimonov et al., 2014). Table 5 presents the PASS predictions for a series of pyridine-4-carbohydrazide derivatives (INH01–INH19), focusing on the most promising activities where Pa > 0.7. These predictions aim to understand the potential therapeutic applications of these compounds, with isoniazid (INH) serving as the template compound.Based on the data, each compound has multiple potential activities, we can classify the activities into several categories:

Core Activities: These activities appear consistently across multiple INH compounds and likely represent the primary mechanisms of action: antituberculosis, antimycobacterial, taurine dehydrogenase inhibitor, and amine dehydrogenase inhibitor.

Secondary Activities: These are activities that appear in multiple INH compounds but with less frequency or probability than the core activities. HMGCS2 expression enhancer (cholesterol metabolism), phosphatidylserine decarboxylase inhibitor (cell signaling), glutamine-phenylpyruvate transaminase inhibitor (amino acid metabolism), antiviral (picornavirus and poxvirus), beta-adrenergic receptor kinase inhibitor (hormonal signaling).

Tertiary Activities: These are less common and often have lower probabilities. Threonine aldolase inhibitor, isopenicillin-N epimerase inhibitor, nicotinamidase inhibitor, PFA-M1 aminopeptidase inhibitor, MCL-1 antagonist,nicotinic alpha6beta3beta4alpha5 receptor antagonist, corticosteroid side-chain-isomerase inhibitor,  phenylalanine(histidine) transaminase inhibitor,  CYP2A8 substrate,  gluconate 2-dehydrogenase (acceptor) inhibitor,  cytoprotectant,  transcription factor stat3 inhibitor, arylalkyl acylamidase inhibitor, aldehyde dehydrogenase (pyrroloquinoline-quinone) inhibitor, thiol protease inhibitor, neuropeptide y2 antagonist, aspartate-phenylpyruvate transaminase inhibitor, phthalate 4,5-dioxygenase inhibitor, glycosylphosphatidylinositol phospholipase D inhibitor. Additionally, the analysis of predicted activities across the INH derivatives reveals some interesting structure-activity relationships (SARs). Antibacterial activity, a primary focus due to INH’s established efficacy against Mycobacterium tuberculosis and Gram-positive bacteria, showed that many derivatives had comparable or even superior potency to INH. Structural modifications appear to enhance antibacterial potency, especially with para-substitution on the phenyl ring, which was particularly effective for antimycobacterial activity. Derivatives such as INH04, INH07, INH08, INH12, and INH15 exhibited higher activity with para-substituted phenyl groups. Electron-donating groups (e.g., -OCH3, -N(CH3)2) further improved activity, as seen in INH07, INH08, and INH14, while electron-withdrawing groups (e.g., -NO2, -Cl) also boosted potency, as observed in INH03 and INH12. Compounds containing furan (INH13) or thiophene (INH16) substituents showed strong antibacterial activity, particularly against tuberculosis. In contrast, ortho- or meta-substitution (INH02, INH05, INH06, INH11) tended to reduce activity compared to para-substituted compounds. Naphthyl substitutions at the 1- or 2-position (INH09, INH14, INH19) did not significantly enhance activity over phenyl derivatives, and the lack of activity in INH14 suggests that bulkier groups may interfere with key binding interactions. Regarding antiviral activity, isoniazid itself demonstrated no potential across viral targets. However, many derivatives exhibited enhanced antiviral properties, underscoring the importance of structural modifications. Derivatives such as INH01, INH03, INH04, INH07, INH08, INH10, INH12, INH15, INH17, and INH19 displayed notable antiviral activity, with INH01, INH07, INH08, INH12, and INH15 showing efficacy against Picornavirus (PV) and Poxvirus (POV). Similar to the antibacterial SARs, para-substitution on the phenyl ring was again favored for antiviral activity. Electron-donating groups (e.g., -OCH3, -N(CH3)2) boosted activity in compounds like INH07, INH08, and INH14, while electron-withdrawing groups (e.g., -NO2, -Cl) enhanced activity in INH03 and INH12. Furan (INH13) and thiophene (INH16) substituents showed little antiviral activity, while ortho- or meta-substituted phenyl groups (INH02, INH05, INH06, INH11) performed less effectively. Naphthyl substituents (INH09, INH14, INH19) were not advantageous compared to phenyl groups. INH04, INH07, INH08, INH12, and INH15 emerge as promising candidates for further investigation, warranting in vitro and in vivo studies to confirm their antiviral efficacy and safety. In contrast, INH demonstrated no predicted activity as an enhancer of HMGCS2 expression, a key enzyme in lipid metabolism. However, many derivatives showed increased activity in this area, indicating that structural modifications improved the HMGCS2 expression-enhancing properties. Para-substitution on the phenyl ring was again advantageous for enhancing HMGCS2 expression. Compounds such as INH04, INH07, INH08, INH12, and INH15 showed heightened activity, with electron-donating groups (e.g., -OCH3, -N(CH3)2) further enhancing efficacy, as in INH07, INH08, and INH14. Electron-withdrawing groups (e.g., -NO2, -Cl) also proved beneficial, as seen in INH03 and INH12. INH13, containing a furan substituent, exhibited the highest activity for HMGCS2 expression enhancement, underscoring the importance of this group. As with antibacterial and antiviral activities, ortho- and meta-substitutions (INH02, INH05, INH06, INH11) resulted in reduced activity, while naphthyl substituents (INH09, INH14, INH19) did not significantly improve activity over phenyl groups. Derivatives INH02, INH10, INH12, and INH13 are particularly promising for further exploration in metabolic or transcriptional pathways regulated by HMGCS2.

ADME prediction

As opposed to pharmacodynamics, which describes what the drug does to the body. Pharmacokinetics (PK) describes what the body does to the drug(Currie, 2018a). There are four major determinants of PK, commonly called ADME properties(absorption, distribution, metabolism, and excretion). These properties are crucial for determining the drug's efficacy and safety(Lucas et al., 2019).

The ADME process can be broadly summarized as (i) drug dissolution in the gastrointestinal tract, followed by absorption through the gut wall and passage into the bloodstream via the liver; (ii) distribution of the drug to various tissues, depending on its structural and physicochemical properties; (iii) metabolism, where the drug is biochemically modified into metabolites, often by enzymatic systems, and (iv) elimination of the drug, usually through excretion (Gleeson et al., 2011; van de Waterbeemd& Gifford, 2003). For a compound to be effective it must reach its target in the body at sufficient concentrations and remain in a bioactive form long enough to exert its intended biological effects. Thus, understanding ADME properties early in drug development can help minimize the time, cost, and labor involved by focusing on compounds with promising profiles. This section evaluates the absorption, distribution, metabolism, and excretion characteristics of pyridine-4-carbohydrazide derivatives (INH01–INH19), comparing them to the parent compound isoniazid (INH). 

(A) Absorption 

Absorption refers to the process by which a drug moves from the site of administration into systemic circulation(Currie, 2018b). Several parameters are used to evaluate the absorption potential of drug candidates: water solubility (LogS), human intestinal absorption (HIA), permeability across the Caco-2 cell line (LogPapp), skin permeability (LogKp), and their interactions with P-glycoprotein (P-gp I, II). These factors influence the bioavailability of a compound, especially when administered orally. The results of these parameters are summarized in Table 6A.

Water solubility (logS):Water solubility is critical for drug formulation and absorption, particularly for oral delivery(Barrett et al., 2022;Delaney, 2004). Low solubility can lead to poor bioavailability and impaired absorption, while high solubility can enhance drug dissolution and plasma concentration(Tran et al., 2023). The aqueous solubility of a substance is often expressed as log units of molar solubility (mol/L), or logS. The pyridine-4-carbohydrazide derivatives exhibit varying degrees of water solubility, ranging from -1.8896 to -4.042. Negative values reflect lower solubility. For example, INH01 (-2.076) and INH02 (-2.894) exhibit better solubility than isoniazid (-1.6), possibly due to simpler aromatic substitutions like phenyl (INH01) or hydroxyl groups (INH02), which slightly decrease solubility without significantly impairing absorption. On the other hand, derivatives like INH10 (-4.02), INH14 (-4.042), and INH17 (-3.643) show poor solubility, likely because of bulky or electron-withdrawing groups, such as nitro or naphthyl rings, which hinder aqueous solubility. These low solubility values suggest that these derivatives may struggle with absorption, particularly in aqueous environments.

Human intestinal absorption (HIA):HIA reflects how well a compound is absorbed through the intestinal lining(Azman et al., 2022), and a value over 80% is considered indicative of good absorption(Chander et al., 2017; Pires et al., 2015). The HIA values for the pyridine-4-carbohydrazide derivatives range from 83.21% to 96.43%, indicating that all compounds have good predicted absorption and are favorable for oral bioavailability. INH05 (96.317%) and INH14 (96.436%) show the highest absorption rates, suggesting that their methoxy-hydroxyphenyl and dimethylamino-naphthyl groups, respectively, enhance lipophilicity and passive diffusion through cell membranes. Conversely, INH10 (83.223%) and INH03 (85.889%) demonstrate lower absorption due to the presence of nitro groups, which tend to reduce membrane permeability. Isoniazid itself shows a lower HIA value (75.651%), likely due to its simple structure and lack of lipophilic substituents that facilitate passive absorption.

The Caco-2 cell line (Caco-2):The Caco-2 cell line is often used as a model for intestinal permeability(Kus et al., 2023). Compounds with a LogPapp value greater than 0.90 cm/s are considered to have high permeability(Pires et al., 2015). The pyridine-4-carbohydrazide derivatives show varying permeability values, ranging from -0.1 to 1.386. Compounds such as INH04, INH06, INH07, and INH09 exhibit high permeability, while others, including INH02, INH03, INH10, INH12, INH13, INH14, and INH17, show lower permeability. The reduced permeability of these compounds may be attributed to the presence of polar, bulky, or electron-withdrawing groups, which can impede passive diffusion across intestinal membranes.

Skin permeability (LogKp):Skin permeability reflects the ability of a drug to penetrate the skin barrier, a critical factor for transdermal drug delivery systems(Cordery et al., 2017; Pensado et al., 2022; Tsakovska et al., 2017). Compounds with a LogKp value greater than -2.5 cm/h are considered to have relatively low skin permeability(Pires et al., 2015). The LogKp values of the pyridine-4-carbohydrazide derivatives range from -3.29 to -2.398, suggesting limited potential for transdermal absorption in most compounds. However, INH18, with a LogKp value of -2.398, exhibits the highest skin permeability among the derivatives, indicating that this compound may have potential for transdermal drug delivery.

Permeability glycoprotein (P-gp) interaction:P-gp is a membrane-bound efflux transporter that can limit the bioavailability of drugs by actively pumping them out of cells, particularly in tissues such as the intestines, liver, and brain(Saaby & Brodin, 2017). Compounds that are substrates for P-gp may face reduced bioavailability, as the transporter pumps them out of cells before they can reach therapeutic concentrations(Elmeliegy et al., 2020; Nielsen et al., 2023). Moreover, P-gp substrates can be further categorized into drugs that are not metabolized in humans and those that are substrates for both P-gp and drug-metabolizing enzymes, particularly CYP3A4. Given that many P-gp substrates are also metabolized by CYP3A4, and that P-gp inhibitors often inhibit CYP3A4 as well, numerous drug-drug interactions arise from the inhibition or induction of both P-gp and CYP3A4(Fromm, 2004; König et al., 2013). Several compounds in the study, including INH03, INH07, INH08, INH09, and INH12, are predicted to be substrates for P-gp, suggesting they may face reduced bioavailability due to efflux and possible metabolism by CYP3A4. In contrast, isoniazid and the other derivatives are not expected to interact with P-gp, which may result in better bioavailability by avoiding efflux. Additionally, most compounds are not predicted to inhibit P-gp, except for INH14, which inhibits both P-gp I and II. This inhibition could enhance bioavailability by preventing the efflux of co-administered drugs, making INH14 a potential candidate for combination therapies aimed at overcoming multidrug resistance, particularly in cancer treatments(Côrte-Real et al., 2019; Dong et al., 2020; Waghray & Zhang, 2018).

 

(D) Distribution 

Drug distribution refers to the reversible transfer of a drug within the body, from the bloodstream to various tissues(Berellini et al., 2009; Motl et al., 2006). It plays a crucial role in the ADMET process, as it influences the amount of drug that reaches target sites, affecting both efficacy and potential toxicity(Sun et al., 2022). The distribution properties of derivatives are evaluated using four key parameters: volume of distribution, fraction unbound, blood-brain barrier permeability, and central nervous system permeability, as shown in Table 6B. 

The Volume of Distribution (VDss) quantifies how extensively a drug disperses into body tissues relative to the bloodstream. Higher VDss values (closer to positive) indicate a greater extent of tissue distribution(Hsu et al., 2021). According to Pires et al., a compound is considered to have good tissue distribution if its VDss value exceeds 2.81 L/kg (log VDss> 0.45) and poor distribution if it is below 0.71 L/kg (log VDss< -0.15)(Pires et al., 2015). The VDss values for the derivatives range from -0.432 to 0.212 Log L/kg, suggesting low to moderate tissue distribution. Notably, compounds such as INH02, INH07, INH08, INH09, INH14, INH15, and INH19 exhibit moderate tissue distribution, likely due to their aromatic structures, which enhance lipophilicity compared to isoniazid and other derivatives.

Fraction Unbound (FU) represents the proportion of a drug in the plasma that remains unbound to proteins, with only the unbound fraction being pharmacologically active(Seyfinejad et al., 2021). A higher FU indicates a greater portion of the drug available to exert therapeutic effects(Watanabe et al., 2018). The FU values for the derivatives range from 0.031 to 0.333, with lower FU values corresponding to higher protein binding. Compounds such as INH14, INH19, and INH09, which contain extensive aromatic substituents, and INH03, INH10, and INH17, which contain nitro groups, show lower FU values. In contrast, isoniazid demonstrates the highest FU (0.728), significantly greater than most derivatives, reflecting minimal protein binding and suggesting a higher bioavailability for interaction with target sites.

Blood-Brain Barrier (BBB) Permeability indicates a drug’s ability to cross the BBB, a selective barrier that regulates the entry of substances into the brainCrivori et al., 2000). Compounds with a LogBB> 0.3 are considered capable of crossing the BBB easily, while those with a LogBB< -1.0 face significant barriers(Pires et al., 2015). Most pyridine-4-carbohydrazide derivatives demonstrate low BBB permeability, suggesting limited brain penetration. However, compounds INH15 and INH18 exhibit higher BBB permeability, indicating their potential for greater brain access.

CNS Permeability (Log PS) further evaluates the ability of these compounds to penetrate the central nervous system. Compounds with a Log PS > -2 are considered capable of CNS penetration, while those with a Log PS < -3 are unlikely to cross the CNS barrier(Pires et al., 2015). For the derivatives studied, only INH19 exhibits a Log PS lower than -2, suggesting it has poor CNS penetration. The remaining derivatives exhibit moderate CNS permeability (Log PS between -2 and -3). Isoniazid, with a Log PS of -3.022, shows one of the lowest CNS permeabilities, indicating it is less likely to penetrate the CNS effectively, which aligns with its hydrophilic structure.

3.5.3 | (M) Metabolism

Drug metabolism is the process by which the body's enzymes chemically modify drug molecules. This is a vital defense mechanism against potential toxins, which are often lipid-soluble and can accumulate in the body. To facilitate excretion, these toxins are converted into more water-soluble metabolites.Most drug metabolism occurs in the liver, where enzymes called hepatic microsomal enzymes catalyze the breakdown process.The metabolic properties of pyridine-4-carbohydrazide derivatives were assessed by evaluating their potential as substrates and/or inhibitors of key cytochrome P450 (CYP) enzymes, which are essential detoxifying enzymes predominantly expressed in the liver(Zanger & Schwab, 2013). To date, 57 distinct CYP isoforms have been identified in humans, of which five—CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4—play pivotal roles in drug metabolism(Wei et al., 2024). The metabolic pharmacokinetic characteristics of the compounds are presented in Table 6C. 

Among these isoforms, CYP3A4 and CYP2D6 are of particular clinical importance due to their significant involvement in the metabolism of various drugs. Inhibition of these enzymes can result in reduced drug clearance, elevated drug plasma concentrations, and potential adverse reactions. Conversely, if a compound serves as a substrate for these enzymes, it is likely to be efficiently metabolized, reducing the risk of side effects related to drug accumulation.Several pyridine-4-carbohydrazide derivatives, including INH01, INH03, INH04, INH07, INH08, and INH10, are identified as substrates for CYP3A4, indicating their likelihood of efficient metabolic processing by this enzyme, thereby reducing the risk of accumulation-related side effects. Furthermore, most derivatives exhibit no significant inhibition of CYP2D6, CYP2E1 and CYP3A4, which is advantageous, as it suggests a lower potential for drug-drug interactions and reduced risk of hepatotoxicity. Notably, INH18 acts as a dual substrate for both CYP3A4 and CYP2D6, further lowering the probability of metabolic interactions. Regarding CYP1A2, all derivatives except INH06 inhibit this isoform, potentially resulting in elevated plasma concentrations of co-administered drugs metabolized by CYP1A2 and increasing the risk of drug-drug interactions. The inhibition of CYP1A2 may be linked to the presence of electron-withdrawing substituents, such as nitro and halogen groups, on the aromatic rings of these compounds, which likely facilitate interaction with the enzyme's active site. About CYP2C8, most derivatives inhibit this enzyme, with the exceptions of INH07, INH12, INH13, INH15 and INH16, which do not exhibit inhibitory activity. The inhibition observed in other derivatives may be attributed to structural features, such as bulky substituents, which could impede the enzyme's binding affinity. For CYP2C9, only INH14 shows inhibitory activity, which may be ascribed to the presence of a dimethylaminonaphthyl group that interacts with the active site of CYP2C9. Several derivatives also exhibit inhibition of CYP2C19, including INH04, INH09, INH10, INH14, INH18, and INH19. Structural elements such as nitro groups in INH09 and INH10, and naphthyl groups in INH14 and INH19, likely contribute to this inhibitory activity by introducing steric and electronic effects that influence binding to the CYP2C19 active site.

(E) Excretion

Excretion refers to the process by which drugs are eliminated from the body, and it is closely related to the concentration of the drug in the bloodstream and the rate of elimination(Talevi& Bellera, 2018). In this study, the excretion properties of pyridine-4-carbohydrazide derivatives (INH01–INH19) were evaluated based on their total clearance (CLtot) values, expressed in Log ml/min/kg, and their potential interaction with the Renal Organic Cation Transporter 2 (OCT2), as presented in Table 6D. The total clearance (CLtot) values for the derivatives ranged from 0.027 to 0.966 Log ml/min/kg. Isoniazid, the reference compound, exhibited a moderate clearance rate (Log CLtot = 0.782). Several derivatives showed clearance values close to this, suggesting that, despite structural modifications, the pyridine-4-carbohydrazide core structure contributes to a baseline level of metabolic stability. Derivatives with higher clearance rates (Log CLtot> 0.800), represented in Tuscan color, include INH05, INH06, INH10, INH11, INH14, and INH18. These compounds are cleared more rapidly from the body, indicating potentially shorter half-lives and a possible need for more frequent dosing. Structural features such as electron-withdrawing groups (e.g., nitro groups in INH10 and INH11) or bulky aromatic substituents (e.g., the naphthyl group in INH14) likely enhance the compounds' interaction with metabolic enzymes, leading to increased clearance. In contrast, compounds INH01, INH02, INH03, INH07, INH08, INH09, INH12, INH13, INH15, INH17, INH19, and isoniazid exhibit moderate clearance rates (Log CLtot ~ 0.700–0.800), indicated in green color. These derivatives typically feature less complex aromatic substitutions, which may reduce metabolic enzyme interactions, resulting in slower clearance rates. Compounds with moderate clearance rates are likely to remain in the body for a longer duration, which may enhance their ability to reach and maintain therapeutic levels at target sites. The lowest clearance rates (Log CLtot< 0>

Conclusion

In drug discovery, understanding and predicting the physicochemical properties of compounds is essential to optimize their pharmacokinetic profiles. The pyridine-4-carbohydrazide derivatives examined in this study show a clear trend of improved physicochemical properties compared to the parent compound, isoniazid (INH). These enhancements are attributed to the structural modifications involving diverse aromatic substituents, resulting in derivatives with varied molecular weight, volume, hydrophobicity and hydrogen bonding capacity. Crucially, all examined compounds meet the criteria of Lipinski's Rule of Five and the Veber rule, suggesting they possess favorable drug-likeness characteristics, high permeability and biological availability. Despite their increased molecular complexity, these compounds maintain drug-likeness without violations, reinforcing their potential as drug candidates.  The bioactivity scores of the derivatives demonstrate that structural modifications significantly influence predicted interactions with various biological targets. Unlike isoniazid, which exhibits limited bioactivity across a range of targets, derivatives with aromatic electron-donating groups showed improved bioactivity. On the other hand, the presence of electron-withdrawing groups tends to diminish the compounds' biological activity. This highlights the importance of careful structural modification to optimize bioactivity profiles, as the electronic properties of these groups directly impact target binding affinities. The PASS predictions for the pyridine-4-carbohydrazide derivatives provide key insights into the impact of structural modifications on their pharmacological potential. Compounds such as INH03, INH09, INH14 and INH19 show high predicted activity across various therapeutic categories, positioning them as strong candidates for further experimental investigation. These derivatives demonstrate potential in antibacterial, antiviral, antiprotozoal, anti-inflammatory and anticancer applications. The observed structural modifications create opportunities for the discovery of novel bioactive agents, offering promising avenues for the development of new therapeutic interventions across a range of medical fields.

The pharmacokinetic profiles of the pyridine-4-carbohydrazide derivatives also highlight the influence of structural modifications on absorption, distribution, metabolism and excretion. Many of the derivatives show improved absorption and permeability profiles compared to isoniazid, particularly those with electron-withdrawing or hydrophobic substituents. However, some derivatives face challenges such as limited solubility or interactions with P-glycoprotein (P-gp), necessitating further optimization to overcome these absorption barriers. In terms of distribution, bulky and lipophilic derivatives exhibit greater tissue penetration and blood-brain barrier permeability, suggesting their potential utility in treating CNS-related conditions. However, these modifications may also increase the risk of CNS side effects, requiring a balanced approach to design. Metabolic profiling highlights the influence of specific substituents on interactions with cytochrome P450 enzymes, with some derivatives, such as INH14, exhibiting enhanced CYP450 inhibitory activity. This underscores the need to carefully consider structural modifications to minimize drug-drug interaction risks. Excretion rates also vary, with derivatives showing different clearance values. Those with higher clearance rates may require more frequent dosing, while others with lower clearance could risk accumulation. The absence of renal OCT2 interactions suggests hepatic clearance pathways, reducing the risk of renal toxicity. Overall, while these derivatives show promising pharmacological and pharmacokinetic profiles, further optimization and experimental validation will be essential to fully realize their therapeutic potential.

Declaration of Competing Interest

All authors have made significant contributions to this manuscript, as follows: (a) conception, design, analysis, and interpretation of the data; (b) drafting the manuscript or critically revising it for significant intellectual content and (c) approving the final version to be published.

Sofian S. Mohamed led the study’s design, investigation, data interpretation, and initial drafting.Salah M. Bensaber provided supervision and contributed to the study design.Nisreen H. Meiqaland Anton Hermann participated in reviewing and editing the manuscript. Abdul M. Gbaj contributed through supervision, validation and drafting of the manuscript.

All authors have reviewed and approved the final version of the manuscript. The authors confirm that this manuscript has not been submitted to, nor is it under review at, any other journal or publication venue.

Acknowledgments

We would like to thank the team of Judicial Expertise and Research Center, Tripoli, Libya for technical support throughout this project. We also thank Sir consultant Khaled Abuajila Diab (CEO of Judicial Expertise and Research Center, Tripoli, Libya) for his valuable support while performing this project.

Ethical approval

All the methods were performed under relevant institutional guidelines and regulations. 

Appendix A. Supplementary material

Appendix A. Supplementary material

Supplementary data associated with this article can be found in the online at [XXXXXX]. 

Supplementary material

Table 4A: Physicochemical Properties and Drug-Likeness Scores for the Predicted Compounds.

CodesN atomsLipinski's RuleVeber Rule

N violations

of rule 

of 5

Volume

(Å 3)

MW 

(Da)

mi logPN ONN OHNHN rotb

TPSA

 (Å 2)

INH0117225.251.8141354.350205.94
INH0218241.251.7552374.580213.94
INH0320270.251.74714100.180229.28
INH0418259.702.2641354.350219.48
INH0520268.321.9151454.590239.51
INH0621255.281.8751454.590256.31
INH0720271.281.1562483.820251.85
INH0819285.301.1562583.820231.49
INH0922291.312.9152374.580257.95
INH1022300.271.75815109.140254.82
INH1120271.281.7862483.820239.51
INH1220270.251.77714100.180229.28
INH1316215.211.0751367.490187.51
INH1424318.383.0251457.590295.84
INH1518239.282.2641354.350222.50
INH1616231.281.7141354.350196.65
INH1720270.251.72714100.180229.28
INH1819251.292.0341454.350233.63
INH1921275.312.9941354.350249.93
INH10137.14- 0.9743168.010122.65

Abbreviations: Number of nonhydrogen atoms (N atoms); Molecular Weight (MW); Logarithm of partition Coefficient Between n-octanol and water LogP); Number of hydrogen bond acceptors (N-ON,O, and N atoms); Number of Hydrogen Bond Donors (N-OHNH,OH, and NH groups); Number of Rotatable Bonds (N-rotb),Topological Polar Surface area (TPSA); and Number of violations (N violations); Number of violations (N violations); and Molecular volume (Å3).

Table 4B: Bioactivity score of compounds according to Molinspiratin cheminformatics.

CodesBioactivities score
EIKIGPCRPINRCICM
INH01-0.49-0.59-0.65-0.93-1.02-0.87
INH02-0.40-0.49-0.53-0.75-0.77-0.9
INH03-0.52-0.54-0.62-0.83-0.84-0.82
INH04-0.48-0.55-0.55-0.88-0.94-0.81
INH05-0.38-0.32-0.42-0.68-0.69-0.77
INH06-0.45-0.47-0.54-0.8-0.81-0.87
INH07-0.36-0.36-0.44-0.75-0.66-0.79
INH08-0.38-0.38-0.43-0.7-0.57-0.78
INH09-0.3-0.27-0.31-0.48-0.48-0.67
INH10-0.54-0.47-0.54-0.73-0.75-0.90
INH11-0.33-0.40-0.54-0.66-0.61-0.90
INH12-0.50-0.54-0.61-0.83-0.83-0.80
INH13-0.77-1.00-0.98-1.25-1.55-1.06
INH14-0.23-0.17-0.11-0.40-0.38-0.58
INH15-0.51-0.56-0.62-0.90-0.94-0.91
INH16-0.70-0.99-1.01-1.12-1.48-1.24
INH17-0.59-0.63-0.67-0.84-0.77-0.83
INH18-0.31-0.64-0.35-0.65-0.79-0.81
INH19-0.29-0.26-0.31-0.50-0.59-0.68
INH-0.66-1.05-1.39-1.23-2.33-1.45

Abbreviations: G protein-coupled receptor (GPCR) ligand, Ion channel modulator (ICM), Kinase inhibitor (KI), Nuclear receptor ligand (NRL), Protease inhibitor (PI), Enzyme inhibitor (EI). The gray color represents inactive and the green color represents moderate bioactivity under the scores reflect the predicted bioactivity, with more negative values indicating lower predicted activity for a given target.

Table 5: PASS prediction Properties of the Predicted Compounds.

Codes

Probability of

Activity

(Pa)

Probability of

Activity

(Pi)

Therapeutic Activity
INH010,9130,002Glutamine-phenylpyruvate transaminase inhibitor
0,9080,004Taurine dehydrogenase inhibitor
0,8950,003Amine dehydrogenase inhibitor
0,8800,003Threonine aldolase inhibitor
0,8600,002Antituberculosic
0,8550,004HMGCS2 expression enhancer
0,8530,003Isopenicillin-N epimerase inhibitor
0,8510,008Beta-adrenergic receptor kinase inhibitor
0,8510,008G-protein-coupled receptor kinase inhibitor
0,8400,003Antimycobacterial
0,8220,002Phenylalanine(histidine) transaminase inhibitor
0,8130,003Antiviral (Picornavirus)
0,8120,004Phosphatidylserine decarboxylase inhibitor
0,7760,003PfA-M1 aminopeptidase inhibitor
0,7590,002Serine-pyruvate transaminase inhibitor
0,7480,003Trimethylamine dehydrogenase inhibitor
0,7430,003Nicotinamidase inhibitor
0,7430,004Mcl-1 antagonist
0,7340,008Antiviral (Poxvirus)
0,7200,029Nicotinic alpha6beta3beta4alpha5 receptor antagonist
 
INH020,9010,002Antituberculosic
0,9000,002Threonine aldolase inhibitor
0,8970,004Taurine dehydrogenase inhibitor
0,8910,003Amine dehydrogenase inhibitor
0,8720,003HMGCS2 expression enhancer
0,8660,003Antimycobacterial
0,8620,003Glutamine-phenylpyruvate transaminase inhibitor
0,8310,003Phosphatidylserine decarboxylase inhibitor
0,8360,009Beta-adrenergic receptor kinase inhibitor
0,8360,009G-protein-coupled receptor kinase inhibitor
0,7690,003Nicotinamidase inhibitor
0,7680,003PfA-M1 aminopeptidase inhibitor
0,7670,004Isopenicillin-N epimerase inhibitor
0,7250,004Mcl-1 antagonist
0,7230,007Corticosteroid side-chain-isomerase inhibitor
0,7150,005Antiviral (Picornavirus)
0,7090,004Phenylalanine(histidine) transaminase inhibitor
 
INH030,9300,002Antituberculosic
0,8960,003Antimycobacterial
0,8630,002Antiviral (Picornavirus)
0,8540,003Phosphatidylserine decarboxylase inhibitor
0,8510,004HMGCS2 expression enhancer
0,8390,008Taurine dehydrogenase inhibitor
0,8010,003Nicotinamidase inhibitor
0,7750,005Glutamine-phenylpyruvate transaminase inhibitor
0,7470,005Amine dehydrogenase inhibitor
0,7420,003PfA-M1 aminopeptidase inhibitor
0,7370,004Mcl-1 antagonist
 
INH040,9090,003Taurine dehydrogenase inhibitor
0,8810,003Amine dehydrogenase inhibitor
0,8600,003Glutamine-phenylpyruvate transaminase inhibitor
0,8440,003Antituberculosic
0,8440,004HMGCS2 expression enhancer
0,8390,003Antimycobacterial
0,8040,013G-protein-coupled receptor kinase inhibitor
0,8040,013Beta-adrenergic receptor kinase inhibitor
0,7880,003Antiviral (Picornavirus)
0,7820,005Threonine aldolase inhibitor
0,7510,003PfA-M1 aminopeptidase inhibitor
0,7090,005Isopenicillin-N epimerase inhibitor
0,7050,004Phenylalanine(histidine) transaminase inhibitor
0,7050,007CYP2A8 substrate
0,7080,011Phosphatidylserine decarboxylase inhibitor
0,7030,009Antiviral (Poxvirus)
0,7480,055Phobic disorders treatment
 
INH050,8900,002Antituberculosic
0,8710,003Antimycobacterial
0,8460,004Amine dehydrogenase inhibitor
0,8430,007Taurine dehydrogenase inhibitor
0,8140,004Threonine aldolase inhibitor
0,7600,007HMGCS2 expression enhancer
0,7480,003Cytoprotectant
0,7480,018Beta-adrenergic receptor kinase inhibitor
0,7480,018G-protein-coupled receptor kinase inhibitor
0,7270,009Phosphatidylserine decarboxylase inhibitor
0,7210,009Glutamine-phenylpyruvate transaminase inhibitor
 
INH060,9130,002Antituberculosic
0,8800,005G-protein-coupled receptor kinase inhibitor
0,8800,005Beta-adrenergic receptor kinase inhibitor
0,8730,003Antimycobacterial
0,7520,006Phosphatidylserine decarboxylase inhibitor
0,7360,004Cytoprotectant
0,7270,008Threonine aldolase inhibitor
0,7090,009HMGCS2 expression enhancer
0,7060,030Taurine dehydrogenase inhibitor
 
INH070,9460,002Taurine dehydrogenase inhibitor
0,8000,004Antimycobacterial
0,8000,005Glutamine-phenylpyruvate transaminase inhibitor
0,7970,003Antituberculosic
0,7980,005HMGCS2 expression enhancer
0,7870,005Amine dehydrogenase inhibitor
0,7800,005Threonine aldolase inhibitor
0,7590,006Phosphatidylserine decarboxylase inhibitor
0,7520,004Antiviral (Picornavirus)
0,7430,008Antiviral (Poxvirus)
0,7190,021Beta-adrenergic receptor kinase inhibitor
0,7190,021G-protein-coupled receptor kinase inhibitor
 
INH080,8950,003Amine dehydrogenase inhibitor
0,8890,004Taurine dehydrogenase inhibitor
0,8470,003Antimycobacterial
0,8450,003Antituberculosic
0,8110,012Beta-adrenergic receptor kinase inhibitor
0,8110,012G-protein-coupled receptor kinase inhibitor
0,8030,005HMGCS2 expression enhancer
0,8000,005Glutamine-phenylpyruvate transaminase inhibitor
0,7660,005Threonine aldolase inhibitor
0,7470,004Antiviral (Picornavirus)
0,7440,035Gluconate 2-dehydrogenase (acceptor) inhibitor
0,7100,004Cytoprotectant
 
INH090,8650,005Taurine dehydrogenase inhibitor
0,8640,003Amine dehydrogenase inhibitor
0,8620,002Antituberculosic
0,8510,003Threonine aldolase inhibitor
0,8480,003Antimycobacterial
0,7990,005Glutamine-phenylpyruvate transaminase inhibitor
0,7920,005HMGCS2 expression enhancer
0,7730,005Phosphatidylserine decarboxylase inhibitor
0,7430,004Isopenicillin-N epimerase inhibitor
0,7300,020Beta-adrenergic receptor kinase inhibitor
0,7300,020G-protein-coupled receptor kinase inhibitor
0,7100,004Cytoprotectant
 
INH100,9240,002Antituberculosic
0,9040,002Antimycobacterial
0,8650,003HMGCS2 expression enhancer
0,7780,016Taurine dehydrogenase inhibitor
0,7540,006Phosphatidylserine decarboxylase inhibitor
0,7410,004Transcription factor STAT3 inhibitor
0,7080,003PfA-M1 aminopeptidase inhibitor
0,7080,005Antiviral (Picornavirus)
 
INH110,8910,002Antituberculosic
0,8790,003Amine dehydrogenase inhibitor
0,8740,003Antimycobacterial
0,8630,005Taurine dehydrogenase inhibitor
0,8390,004HMGCS2 expression enhancer
0,8090,004Threonine aldolase inhibitor
0,7920,014G-protein-coupled receptor kinase inhibitor
0,7920,014Beta-adrenergic receptor kinase inhibitor
0,7760,003Cytoprotectant
0,7230,009Phosphatidylserine decarboxylase inhibitor
0,7160,003PfA-M1 aminopeptidase inhibitor
0,7140,009Glutamine-phenylpyruvate transaminase inhibitor
0,7220,045Gluconate 2-dehydrogenase (acceptor) inhibitor
 
INH120,9280,002Antituberculosic
0,8950,003Antimycobacterial
0,8720,002Phosphatidylserine decarboxylase inhibitor
0,8640,003HMGCS2 expression enhancer
0,8620,005Taurine dehydrogenase inhibitor
0,8540,003Antiviral (Picornavirus)
0,8450,002Nicotinamidase inhibitor
0,8230,004Glutamine-phenylpyruvate transaminase inhibitor
0,7890,004Amine dehydrogenase inhibitor
0,7620,004Mcl-1 antagonist
0,7530,003PfA-M1 aminopeptidase inhibitor
0,7180,008Threonine aldolase inhibitor
0,7180,009Antiviral (Poxvirus)
0,7180,040Acrocylindropepsin inhibitor
0,7180,040Chymosin inhibitor
0,7180,040Saccharopepsin inhibitor
 
INH130,9560,001HMGCS2 expression enhancer
0,9280,002Antituberculosic
0,9130,003Mcl-1 antagonist
0,9040,002Antimycobacterial
0,7960,005Glutamine-phenylpyruvate transaminase inhibitor
0,7920,003Isopenicillin-N epimerase inhibitor
0,7910,004Amine dehydrogenase inhibitor
0,7610,003PfA-M1 aminopeptidase inhibitor
0,7480,003Neuropeptide Y2 antagonist
0,7460,022Taurine dehydrogenase inhibitor
0,7200,008Threonine aldolase inhibitor
0,7070,004Amyloid beta precursor protein antagonist
 
INH140,9120,003Taurine dehydrogenase inhibitor
0,7620,006HMGCS2 expression enhancer
0,7020,008Amine dehydrogenase inhibitor
 
INH150,8890,004Taurine dehydrogenase inhibitor
0,8760,003Glutamine-phenylpyruvate transaminase inhibitor
0,8670,003Amine dehydrogenase inhibitor
0,8660,002Antituberculosic
0,8470,003Antimycobacterial
0,8300,004HMGCS2 expression enhancer
0,8080,004Threonine aldolase inhibitor
0,8130,012G-protein-coupled receptor kinase inhibitor
0,8130,012Beta-adrenergic receptor kinase inhibitor
0,8040,003Isopenicillin-N epimerase inhibitor
0,8040,004Phosphatidylserine decarboxylase inhibitor
0,7610,004Antiviral (Picornavirus)
0,7540,003PfA-M1 aminopeptidase inhibitor
0,7400,004Phenylalanine(histidine) transaminase inhibitor
0,7100,003Serine-pyruvate transaminase inhibitor
0,7110,009Antiviral (Poxvirus)
 
INH160,7960,005Glutamine-phenylpyruvate transaminase inhibitor
0,7710,004CYP2E1 inhibitor
0,7610,004Antimycobacterial
0,7590,004Antituberculosic
0,7530,004Mcl-1 antagonist
0,7470,022Taurine dehydrogenase inhibitor
0,7210,003PfA-M1 aminopeptidase inhibitor
 
INH170,8610,005Taurine dehydrogenase inhibitor
0,8450,004HMGCS2 expression enhancer
0,8410,003Phosphatidylserine decarboxylase inhibitor
0,8380,003Antituberculosic
0,8090,004Antimycobacterial
0,8000,005Glutamine-phenylpyruvate transaminase inhibitor
0,7880,004Amine dehydrogenase inhibitor
0,7830,004Arylalkyl acylamidase inhibitor
0,7760,004Aldehyde dehydrogenase (pyrroloquinoline-quinone) inhibitor
0,7680,003Nicotinamidase inhibitor
0,7650,004Antiviral (Picornavirus)
0,7340,003Phenylalanine racemase (ATP-hydrolysing) inhibitor
0,7080,009Threonine aldolase inhibitor
 
INH180,8600,003Glutamine-phenylpyruvate transaminase inhibitor
0,8410,004Amine dehydrogenase inhibitor
0,8360,008Taurine dehydrogenase inhibitor
0,8240,003Antituberculosic
0,8070,004Antimycobacterial
0,8020,004Threonine aldolase inhibitor
0,7610,004Thiol protease inhibitor
0,7500,004Isopenicillin-N epimerase inhibitor
0,7260,009Phosphatidylserine decarboxylase inhibitor
0,7100,004Mcl-1 antagonist
0,7050,004Phenylalanine(histidine) transaminase inhibitor
 
INH190,8830,004Taurine dehydrogenase inhibitor
0,8760,003Glutamine-phenylpyruvate transaminase inhibitor
0,8610,003Amine dehydrogenase inhibitor
0,8380,003Isopenicillin-N epimerase inhibitor
0,8250,004Threonine aldolase inhibitor
0,8250,004HMGCS2 expression enhancer
0,7700,003Antituberculosic
0,7530,004Antimycobacterial
0,7510,006Phosphatidylserine decarboxylase inhibitor
0,7400,004Phenylalanine(histidine) transaminase inhibitor
0,7150,005Antiviral (Picornavirus)
 
INH0,9680,001Taurine dehydrogenase inhibitor
0,9260,001Trimethylamine dehydrogenase inhibitor
0,8470,004Amine dehydrogenase inhibitor
0,8420,003Isopenicillin-N epimerase inhibitor
0,8250,004Aldehyde dehydrogenase (pyrroloquinoline-quinone) inhibitor
0,8160,004Arylalkyl acylamidase inhibitor
0,8100,003Antituberculosic
0,7980,004Antimycobacterial
0,7900,003Nitrilase inhibitor
0,7820,002Maillard reaction inhibitor
0,7810,014Nicotinic alpha6beta3beta4alpha5 receptor antagonist
0,7610,001Aralkylamine dehydrogenase inhibitor
0,7740,015Glucose oxidase inhibitor
0,7540,008Manganese peroxidase inhibitor
0,7570,013Arylacetonitrilase inhibitor
0,7580,016Nicotinic alpha2beta2 receptor antagonist
0,7380,004N-methylhydantoinase (ATP-hydrolysing) inhibitor
0,7390,009Nucleoside oxidase (H2O2-forming) inhibitor
0,7300,004Nicotinate dehydrogenase inhibitor
0,7240,010Peroxidase inhibitor
0,7390,030Nootropic
0,7130,008Aspartate-phenylpyruvate transaminase inhibitor
0,7120,013Phthalate 4,5-dioxygenase inhibitor
0,7160,031Glycosylphosphatidylinositol phospholipase D inhibitor
0,7010,076Phobic disorders treatment

Abbreviations: Tuscan shading indicates the probability of activity values, and Gray represents the probability of inactivity values.

Table 6A: Absorption Properties of the Predicted Compounds.

CodesLogSHIA  Caco-2LogKp

P-gp

subs

P-gp I

Inhi

P-gp II

Inhi

INH01-2.07693.9541.329-2.682NoNoNo
INH02-2.89493.9740.745-3.29NoNoNo
INH03-3.64185.889-0.1-2.67YesNoNo
INH04-3.01193.2111.353-2.677NoNoNo
INH05-2.95796.3171.328-2.767NoNoNo
INH06-3.00395.6291.382-2.713NoNoNo
INH07-3.57294.6951.386-3.06YesNoNo
INH08-3.00494.2761.369-3.048YesNoNo
INH09-3.32294.2231.086-2.778YesNoNo
INH10-4.0283.2230.265-2.742NoNoNo
INH11-3.01394.8581.197-2.909NoNoNo
INH12-3.05285.9210.194-2.68YesNoNo
INH13-1.889695.9670.801-3.116NoNoNo
INH14-4.04296.436-4.042-2.678NoYesYes
INH15-3.23795.0121.322-2.568NoNoNo
INH16-3.32193.5371.317-2.694NoNoNo
INH17-3.64384.540.235-2.79NoNoNo
INH18-3.41592.8131.35-2.398NoNoNo
INH19-3.55494.0211.374-2.518YesNoNo
INH-1.675.6510.627-3.173NoNoNo

Abbreviations: Water solubility (logS, log mol/L), Human Intestinal Absorption (HIA, %), Human colon epithelial cancer cell line (Caco-2, Log Papp; log cm/s), Skin permeability (LogKp; cm/h), Permeability glycoprotein I, II (P-gp I, II). Gray shading indicates low values, green represents moderate values, and Tuscan signifies high values. Pink shading and "Yes" denote an effect on the target, while white shading and "No" indicate no effect on the target.

Table 6B: Distribution Properties of the Predicted Compounds.

CodesVDssFU  LogBBLog PS
INH01-0.3490.2040.205-2.252
INH02-0.1490.333-0.184-2.497
INH03-0.2320.076-0.518-2.484
INH04-0.2760.2030.146-2.141
INH05-0.2010.2620.252-2.805
INH06-0.4070.2210.22-2.859
INH07-0.0970.214-0.38-2.947
INH08-0.0150.17-0.404-2.946
INH090.1140.0980.053-2.216
INH10-0.3290.071-0.854-2.742
INH11-0.3220.227-0.479-2.986
INH12-0.2770.118-0.553-2.491
INH13-0.6120.45-0.3-2.881
INH140.2120.0310.271-2.108
INH15-0.0490.2010.34-2.254
INH16-0.2980.2930.261-2.836
INH17-0.4310.089-0.574-2.472
INH18-0.0120.1070.317-2.246
INH190.0810.0670.208-1.973
INH-0.4320.728-0.117-3.022

Abbreviations: Distribution Volume in Humans (VDSS, Log L/kg), Fraction Unbound (FU), Blood blood-brain barrier Permeability (LogBB), and Central Nervous System Permeability (LogPS). Gray shading indicates low values, green represents moderate values, and Tuscan signifies high values. 

Table 6C:  Metabolism Properties of the Predicted Compounds.

Codes

CYP

1A2

CYP

3A4

CYP

2C9

CYP

2C19

CYP

2D6

InhiInhiSubsInhiInhiInhiSubs
INH01YesNoYesNoNoNoNo
INH02YesNoNoNoNoNoNo
INH03YesNoYesNoNoNoNo
INH04YesNoYesNoYesNoNo
INH05YesNoNoNoNoNoNo
INH06NoNoYesNoNoNoNo
INH07YesNoYesNoNoNoNo
INH08YesNoYesNoNoNoNo
INH09YesNoYesNoYesNoNo
INH10YesNoYesNoYesNoNo
INH11YesNoNoNoNoNoNo
INH12YesNoYesNoNoNoNo
INH13YesNoNoNoNoNoNo
INH14YesNoYesYesYesNoNo
INH15YesNoYesNoNoNoNo
INH16YesNoNoNoNoNoNo
INH17YesNoYesNoNoNoNo
INH18YesYesYesNoYesNoNo
INH19YesNoYesNoYesNoNo
INHNoNoNoNoNoNoNo

Abbreviations: CYP (Cytochrome P450), Inhi (Inhibitor), Subs (Substrate). The pink color and "Yes" indicate an effect on the target, while the white color and "No" indicate no effect on the target.

Table 6D: Excretion Properties of the Predicted Compounds.

CodesExcretion

CLtot

Log ml/min/kg

Renal OCT2 substrate

(Yes/ No)

INH010.717No
INH020.665No
INH030.744No
INH04-0.054No
INH050.873No
INH060.822No
INH070.771No
INH080.821No
INH090.684No
INH100.834No
INH110.841No
INH120.712No
INH130.757No
INH140.966No
INH150.759No
INH160.027No
INH170.691No
INH180.834No
INH190.678No
INH0.782No

Abbreviations:

Total clearance (CLtot),Organic cation transporter 2 (OCT2). The gray color represents low, the green color represents moderate, and the Tuscan color represents high clearance in accordance with the value by Log ml/min/kg.

References

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George Varvatsoulias

Dear editorial department: On behalf of our team, I hereby certify the reliability and superiority of the International Journal of Clinical Case Reports and Reviews in the peer review process, editorial support, and journal quality. Firstly, the peer review process of the International Journal of Clinical Case Reports and Reviews is rigorous, fair, transparent, fast, and of high quality. The editorial department invites experts from relevant fields as anonymous reviewers to review all submitted manuscripts. These experts have rich academic backgrounds and experience, and can accurately evaluate the academic quality, originality, and suitability of manuscripts. The editorial department is committed to ensuring the rigor of the peer review process, while also making every effort to ensure a fast review cycle to meet the needs of authors and the academic community. Secondly, the editorial team of the International Journal of Clinical Case Reports and Reviews is composed of a group of senior scholars and professionals with rich experience and professional knowledge in related fields. The editorial department is committed to assisting authors in improving their manuscripts, ensuring their academic accuracy, clarity, and completeness. Editors actively collaborate with authors, providing useful suggestions and feedback to promote the improvement and development of the manuscript. We believe that the support of the editorial department is one of the key factors in ensuring the quality of the journal. Finally, the International Journal of Clinical Case Reports and Reviews is renowned for its high- quality articles and strict academic standards. The editorial department is committed to publishing innovative and academically valuable research results to promote the development and progress of related fields. The International Journal of Clinical Case Reports and Reviews is reasonably priced and ensures excellent service and quality ratio, allowing authors to obtain high-level academic publishing opportunities in an affordable manner. I hereby solemnly declare that the International Journal of Clinical Case Reports and Reviews has a high level of credibility and superiority in terms of peer review process, editorial support, reasonable fees, and journal quality. Sincerely, Rui Tao.

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Rui Tao

Clinical Cardiology and Cardiovascular Interventions I testity the covering of the peer review process, support from the editorial office, and quality of the journal.

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Khurram Arshad

Clinical Cardiology and Cardiovascular Interventions, we deeply appreciate the interest shown in our work and its publication. It has been a true pleasure to collaborate with you. The peer review process, as well as the support provided by the editorial office, have been exceptional, and the quality of the journal is very high, which was a determining factor in our decision to publish with you.

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Gomez Barriga Maria Dolores

The peer reviewers process is quick and effective, the supports from editorial office is excellent, the quality of journal is high. I would like to collabroate with Internatioanl journal of Clinical Case Reports and Reviews journal clinically in the future time.

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Lin Shaw Chin

Clinical Cardiology and Cardiovascular Interventions, I would like to express my sincerest gratitude for the trust placed in our team for the publication in your journal. It has been a true pleasure to collaborate with you on this project. I am pleased to inform you that both the peer review process and the attention from the editorial coordination have been excellent. Your team has worked with dedication and professionalism to ensure that your publication meets the highest standards of quality. We are confident that this collaboration will result in mutual success, and we are eager to see the fruits of this shared effort.

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Maria Dolores Gomez Barriga

Dear Dr. Jessica Magne, Editorial Coordinator 0f Clinical Cardiology and Cardiovascular Interventions, I hope this message finds you well. I want to express my utmost gratitude for your excellent work and for the dedication and speed in the publication process of my article titled "Navigating Innovation: Qualitative Insights on Using Technology for Health Education in Acute Coronary Syndrome Patients." I am very satisfied with the peer review process, the support from the editorial office, and the quality of the journal. I hope we can maintain our scientific relationship in the long term.

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Dr Maria Dolores Gomez Barriga

Dear Monica Gissare, - Editorial Coordinator of Nutrition and Food Processing. ¨My testimony with you is truly professional, with a positive response regarding the follow-up of the article and its review, you took into account my qualities and the importance of the topic¨.

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Dr Maria Regina Penchyna Nieto

Dear Dr. Jessica Magne, Editorial Coordinator 0f Clinical Cardiology and Cardiovascular Interventions, The review process for the article “The Handling of Anti-aggregants and Anticoagulants in the Oncologic Heart Patient Submitted to Surgery” was extremely rigorous and detailed. From the initial submission to the final acceptance, the editorial team at the “Journal of Clinical Cardiology and Cardiovascular Interventions” demonstrated a high level of professionalism and dedication. The reviewers provided constructive and detailed feedback, which was essential for improving the quality of our work. Communication was always clear and efficient, ensuring that all our questions were promptly addressed. The quality of the “Journal of Clinical Cardiology and Cardiovascular Interventions” is undeniable. It is a peer-reviewed, open-access publication dedicated exclusively to disseminating high-quality research in the field of clinical cardiology and cardiovascular interventions. The journal's impact factor is currently under evaluation, and it is indexed in reputable databases, which further reinforces its credibility and relevance in the scientific field. I highly recommend this journal to researchers looking for a reputable platform to publish their studies.

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Dr Marcelo Flavio Gomes Jardim Filho

Dear Editorial Coordinator of the Journal of Nutrition and Food Processing! "I would like to thank the Journal of Nutrition and Food Processing for including and publishing my article. The peer review process was very quick, movement and precise. The Editorial Board has done an extremely conscientious job with much help, valuable comments and advices. I find the journal very valuable from a professional point of view, thank you very much for allowing me to be part of it and I would like to participate in the future!”

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Zsuzsanna Bene

Dealing with The Journal of Neurology and Neurological Surgery was very smooth and comprehensive. The office staff took time to address my needs and the response from editors and the office was prompt and fair. I certainly hope to publish with this journal again.Their professionalism is apparent and more than satisfactory. Susan Weiner

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Dr Susan Weiner

My Testimonial Covering as fellowing: Lin-Show Chin. The peer reviewers process is quick and effective, the supports from editorial office is excellent, the quality of journal is high. I would like to collabroate with Internatioanl journal of Clinical Case Reports and Reviews.

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Lin-Show Chin

My experience publishing in Psychology and Mental Health Care was exceptional. The peer review process was rigorous and constructive, with reviewers providing valuable insights that helped enhance the quality of our work. The editorial team was highly supportive and responsive, making the submission process smooth and efficient. The journal's commitment to high standards and academic rigor makes it a respected platform for quality research. I am grateful for the opportunity to publish in such a reputable journal.

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Sonila Qirko

My experience publishing in International Journal of Clinical Case Reports and Reviews was exceptional. I Come forth to Provide a Testimonial Covering the Peer Review Process and the editorial office for the Professional and Impartial Evaluation of the Manuscript.

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Luiz Sellmann

I would like to offer my testimony in the support. I have received through the peer review process and support the editorial office where they are to support young authors like me, encourage them to publish their work in your esteemed journals, and globalize and share knowledge globally. I really appreciate your journal, peer review, and editorial office.

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Zhao Jia

Dear Agrippa Hilda- Editorial Coordinator of Journal of Neuroscience and Neurological Surgery, "The peer review process was very quick and of high quality, which can also be seen in the articles in the journal. The collaboration with the editorial office was very good."

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Thomas Urban

I would like to express my sincere gratitude for the support and efficiency provided by the editorial office throughout the publication process of my article, “Delayed Vulvar Metastases from Rectal Carcinoma: A Case Report.” I greatly appreciate the assistance and guidance I received from your team, which made the entire process smooth and efficient. The peer review process was thorough and constructive, contributing to the overall quality of the final article. I am very grateful for the high level of professionalism and commitment shown by the editorial staff, and I look forward to maintaining a long-term collaboration with the International Journal of Clinical Case Reports and Reviews.

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Cristina Berriozabal

To Dear Erin Aust, I would like to express my heartfelt appreciation for the opportunity to have my work published in this esteemed journal. The entire publication process was smooth and well-organized, and I am extremely satisfied with the final result. The Editorial Team demonstrated the utmost professionalism, providing prompt and insightful feedback throughout the review process. Their clear communication and constructive suggestions were invaluable in enhancing my manuscript, and their meticulous attention to detail and dedication to quality are truly commendable. Additionally, the support from the Editorial Office was exceptional. From the initial submission to the final publication, I was guided through every step of the process with great care and professionalism. The team's responsiveness and assistance made the entire experience both easy and stress-free. I am also deeply impressed by the quality and reputation of the journal. It is an honor to have my research featured in such a respected publication, and I am confident that it will make a meaningful contribution to the field.

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Dr Tewodros Kassahun Tarekegn

"I am grateful for the opportunity of contributing to [International Journal of Clinical Case Reports and Reviews] and for the rigorous review process that enhances the quality of research published in your esteemed journal. I sincerely appreciate the time and effort of your team who have dedicatedly helped me in improvising changes and modifying my manuscript. The insightful comments and constructive feedback provided have been invaluable in refining and strengthening my work".

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Dr Shweta Tiwari

I thank the ‘Journal of Clinical Research and Reports’ for accepting this article for publication. This is a rigorously peer reviewed journal which is on all major global scientific data bases. I note the review process was prompt, thorough and professionally critical. It gave us an insight into a number of important scientific/statistical issues. The review prompted us to review the relevant literature again and look at the limitations of the study. The peer reviewers were open, clear in the instructions and the editorial team was very prompt in their communication. This journal certainly publishes quality research articles. I would recommend the journal for any future publications.

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Dr Farooq Wandroo

Dear Jessica Magne, with gratitude for the joint work. Fast process of receiving and processing the submitted scientific materials in “Clinical Cardiology and Cardiovascular Interventions”. High level of competence of the editors with clear and correct recommendations and ideas for enriching the article.

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Dr Anyuta Ivanova

We found the peer review process quick and positive in its input. The support from the editorial officer has been very agile, always with the intention of improving the article and taking into account our subsequent corrections.

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Dr David Vinyes

My article, titled 'No Way Out of the Smartphone Epidemic Without Considering the Insights of Brain Research,' has been republished in the International Journal of Clinical Case Reports and Reviews. The review process was seamless and professional, with the editors being both friendly and supportive. I am deeply grateful for their efforts.

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Gertraud Teuchert-Noodt

To Dear Erin Aust – Editorial Coordinator of Journal of General Medicine and Clinical Practice! I declare that I am absolutely satisfied with your work carried out with great competence in following the manuscript during the various stages from its receipt, during the revision process to the final acceptance for publication. Thank Prof. Elvira Farina

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Dr Elvira Farina