Phases Of Drug Discovery Research Articles

Screening of the Pandemic Response Box Reveals an Association between Antifungal Effects of MMV1593537 and the Cell Wall of Cryptococcus neoformans, Cryptococcus deuterogattii, and Candida auris.

ABSTRACTThere is an urgent unmet need for novel antifungals. In this study, we searched for novel antifungal activities in the Pandemic Response Box, a collection of 400 structurally diverse compounds in various phases of drug discovery. We identified five molecules which could control the growth of Cryptococcus neoformans, Cryptococcus deuterogattii, and the emerging global threat Candida auris. After eliminating compounds which demonstrated paradoxical antifungal effects or toxicity to mammalian macrophages, we selected compound MMV1593537 as a nontoxic, fungicidal molecule for further characterization of antifungal activity. Scanning electron microscopy revealed that MMV1593537 affected cellular division in all three pathogens. In Cryptococcus, MMV1593537 caused a reduction in capsular dimensions. Treatment with MMV1593537 resulted in increased detection of cell wall chitooligomers in these three species. Since chitooligomers are products of the enzymatic hydrolysis of chitin, we investigated whether surface chitinase activity was altered in response to MMV1593537 exposure. We observed peaks of enzyme activity in C. neoformans and C. deuterogattii in response to MMV1593537. We did not detect any surface chitinase activity in C. auris. Our results suggest that MMV1593537 is a promising, nontoxic fungicide whose mechanism of action, at least in Cryptococcus spp, requires chitinase-mediated hydrolysis of chitin.IMPORTANCE The development of novel antifungals is a matter of urgency. In this study, we evaluated antifungal activities in a collection of 400 molecules, using highly lethal fungal pathogens as targets. One of these molecules, namely, MMV1593537, was not toxic to host cells and controlled the growth of isolates of Cryptococcus neoformans, C. deuterogattii, C. gattii, Candida auris, C. albicans, C. parapsilosis, and C. krusei. We tested the mechanisms of antifungal action of MMV1593537 in the Cryptococcus and C. auris models and concluded that the compound affects the cell wall, a structure which is essential for fungal life. At least in Cryptococcus, this effect involved chitinase, an enzyme which is required for remodeling the cell wall. Our results suggest that MMV1593537 is a candidate for future antifungal development.

Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network.

The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Relative binding free energy (RBFE, also referred to as ΔΔG) methods allow the estimation of binding free energy changes after small changes to a ligand scaffold. Here, we propose and evaluate a Siamese convolutional neural network (CNN) for the prediction of RBFE between two bound ligands. We show that our multitask loss is able to improve on a previous state-of-the-art Siamese network for RBFE prediction via increased regularization of the latent space. The Siamese network architecture is well suited to the prediction of RBFE in comparison to a standard CNN trained on the same data (Pearson's R of 0.553 and 0.5, respectively). When evaluated on a left-out protein family, our Siamese CNN shows variability in its RBFE predictive performance depending on the protein family being evaluated (Pearson's R ranging from -0.44 to 0.97). RBFE prediction performance can be improved during generalization by injecting only a few examples (few-shot learning) from the evaluation data set during model training.

Bacterial Cell Envelope Lysis and Hemotoxicity of Peptides Previously isolated From African Catfish, Clarias gariepinus

Background: The skin mucus layer of fish is endowed with biologics including, Antimicrobial peptides (AMPs) that offer a first line of defence against pathogens. Such peptides can either inhibit bacterial growth or completely kill the bacteria and hence are regarded as a viable alternative to traditional antibiotics, in addressing the ever-increasing incidences of antimicrobial resistance. However, one of the major hurdles to AMPs use is their poor haemolytic profile. As a result, a thorough evaluation of prospective AMPs’ bacterial cell membrane disruption and hemolytic potentials in the early phases of drug discovery is critical. The current study presented cell membrane destruction as well as hemo-compatibility of antimicrobial peptides previously isolated from skin mucus of African catfish, Clarias gariepinus. Methods: A previously isolated antimicrobial peptide in the skin mucus of African catfish, C. gariepinus were profiled using 15% Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE). The electrical conductivity and alkaline phosphatase assays were utilised to measure bacterial cell envelope lysis activity as a classical mode of action of the antimicrobial peptides. Afterwards, fresh Rabbit blood cells were then utilised for in vitro hemolytic assay. Results: The peptides were found to be about 5 kDa molecular weight with, ability to damage the bacterial cell envelope causing significant leakage in periplasmic alkaline phosphatase enzyme and cytoplasmic electrolytes. Even at the highest peptide extract concentration of 100 μg/mL, no significant hemolysis was observed on the fresh rabbit blood cells [3.63%;P>.05], signifying their safety on normal mammalian cells. Conclusion: The findings of this study pointed out that antimicrobial peptides in skin mucus of C. gariepinus are potentially safe source of antimicrobial drug leads; however, further studies are still required to search for possibly maximum dose that is safe to host cells but still effective against infecting bacteria.

Estimation of Fraction Metabolized by Cytochrome P450 Enzymes Using Long-Term Cocultured Human Hepatocytes.

Estimation of the fraction of a drug metabolized by individual hepatic CYP enzymes relative to hepatic metabolism (fm,CYP) or total clearance h as been challenging for low turnover compounds due to insufficient resolution of the intrinsic clearance (CLint) measurement in vitro and difficulties in quantifying the formation of low abundance metabolites. To overcome this gap, inhibition of drug depletion or selective metabolite formation for 7 marker CYP substrates was investigated using chemical inhibitors and a micro-patterned hepatocyte coculture system (HepatoPac). The use of 3 μM itraconazole was successfully validated for estimation of fm,CYP3A4 by demonstration of fm values within a 2-fold of in vivo estimates for 10 out of 13 CYP3A4 substrates in a reference set of marketed drugs. Other CYP3A4 inhibitors (ketoconazole and posaconazole) were not optimal for estimation of fm,CYP3A4 for low turnover compounds due to their high CLint. The current study also demonstrated that selective inhibition sufficient for fm calculation was achieved by inhibitors of CYP1A2 (20 μM furafylline), CYP2C8 (40 μM montelukast), CYP2C9 (40 μM sulfaphenazole), CYP2C19 [3 μM (-)N-3-benzyl-phenobarbital], and CYP2D6 (5 μM quinidine). Good estimation of fm,CYP2B6 was not possible in this study due to the poor selectivity of the tested inhibitor (20 μM ticlopidine). The approach verified in this study can result in an improved fm estimation that is aligned with the regulatory agencies' guidance and can support a victim drug-drug interaction risk assessment strategy for low clearance discovery and development drug candidates. SIGNIFICANCE STATEMENT: Successful qualification of a chemical inhibition assay for estimation of fraction metabolized requires chemical inhibitors that retain sufficient unbound concentrations over time in the incubates. The current cocultured hepatocyte assay enabled estimation of fraction metabolized, especially by CYP3A4, during the drug discovery phase where metabolite quantification methods may not be available. The method enables the assessment of pharmacokinetic variability and victim drug-drug interaction risks due to enzyme polymorphism or inhibition/induction with more confidence, especially for low clearance drug candidates.

Artificial intelligence in drug discovery: Current applications and future directions

Background: The drug discovery process is complex, time-consuming, and costly, traditionally relying on trial-and-error approaches. The integration of artificial intelligence (AI) and machine learning (ML) has emerged as a transformative solution, enhancing efficiency and precision in identifying potential drug candidates. Aim: This review aims to explore the current applications of AI in drug discovery, highlight the AI tools utilized in the process, and discuss the associated challenges. Methods: A comprehensive literature review was conducted, focusing on peer-reviewed articles, clinical studies, and case reports that detail the application of AI and ML in various phases of drug discovery, including target identification, lead optimization, and preclinical evaluation. Results: The review identifies several AI applications, such as predictive modeling, molecular design, and virtual screening, which significantly expedite the discovery process. Tools such as deep learning, natural language processing, and reinforcement learning have been instrumental in analyzing large datasets and predicting drug interactions. However, challenges remain, including data integration issues, skill gaps among professionals, and skepticism regarding AI outcomes. Conclusion: AI has the potential to revolutionize drug discovery by streamlining processes and improving accuracy.

Improving translational power in antischistosomal drug discovery.

Schistosomiasis is a poverty-associated tropical disease caused by blood dwelling trematodes that threaten approximately 10% of the world population. Praziquantel, the sole drug currently available for treatment, is insufficient to eliminate the disease and the clinical drug development pipeline is empty. Here, we review the characteristics of the patent Schistosoma mansoni mouse model used for in vivo antischistosomal drug discovery, highlighting differences in the experimental set-up across research groups and their potential influence on experimental results. We explore the pharmacokinetic/pharmacodynamic relationship of selected drug candidates, showcasing opportunities to improve the drug profile to accelerate the transition from the early drug discovery phase to new clinical candidates.

Artificial Intelligence in Drug Safety and Metabolism.

The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligence can be used in every phase of drug discovery and development, from profiling chemical libraries in early discovery, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life cycle management. This chapter provides an overview of artificial intelligence in drug safety and describes its application throughout the entire discovery and development process.

Application of in vitro PAMPA technique and in silico computational methods for blood-brain barrier permeability prediction of novel CNS drug candidates

Permeability assessment of small molecules through the blood-brain barrier (BBB) plays a significant role in the development of effective central nervous system (CNS) drug candidates. Since in vivo methods for BBB permeability estimation require a lot of time and resources, in silico and in vitro approaches are becoming increasingly popular nowadays for faster and more economical predictions in early phases of drug discovery. In this work, through application of in vitro parallel artificial membrane permeability assay (PAMPA-BBB) and in silico computational methods we aimed to examine the passive permeability of eighteen compounds, which affect serotonin and dopamine levels in the CNS. The data set was consisted of novel six human dopamine transporter (hDAT) substrates that were previously identified as the most promising lead compounds for further optimisation to achieve neuroprotective effect, twelve approved CNS drugs, and their related compounds. Firstly, PAMPA methods was used to experimentally determine effective BBB permeability (Pe) for all studied compounds and obtained results were further submitted for quantitative structure permeability relationship (QSPR) analysis. QSPR models were built by using three different statistical methods: stepwise multiple linear regression (MLR), partial least square (PLS), and support-vector machine (SVM), while their predictive capability was tested through internal and external validation. Obtained statistical parameters (MLR- R2pred=-0.10; PLS- R2pred=0.64, r2m=0.69, r/2m=0.44; SVM- R2pred=0.57, r2m=0.72, r/2m=0.55) indicated that the SVM model is superior over others. The most important molecular descriptors (H0p and SolvEMt_3D) were identified and used to propose structural modifications of the examined compounds in order to improve their BBB permeability. Moreover, steered molecular dynamics (SMD) simulation was employed to comprehensively investigate the permeability pathway of compounds through a lipid bilayer. Taken together, the created QSPR model could be used as a reliable and fast pre-screening tool for BBB permeability prediction of structurally related CNS compounds, while performed MD simulations provide a good foundation for future in silico examination.

Mechanism of activation and the rewired network: New drug design concepts.

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same‐allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure‐based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor‐specific network, ushering innovative considerations in precision medicine.

Mass spectrometry imaging in zebrafish larvae for assessing drug safety and metabolism.

Drug safety assessment in the early phases of drug discovery is critical to facilitate the rapid development of novel therapeutics. Recently, teleost zebrafish (Danio rerio) has emerged as a promising vertebrate model for the assessment of drug safety. Zebrafish is a convenient model because of its small size, high fecundity, embryo transparency, and ex utero development. In this study, we developed a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) method applied to zebrafish larvae to investigate safety and metabolism of sahaquine (Sq), an anticancer agent inhibiting histone deacetylase 6. This technique improves on prior studies using liquid chromatography-mass spectrometry (LC-MS) by adding analysis of the drug spatial distribution. Using this method, it was determined that Sq dissolved in fish water (1-2000μM) did not reach the larval body and was mainly distributed throughout the yolk. High Sq concentration (800μM) administered intravenously allowed the compound to reach the larval body but did not induce phenotypic abnormalities. Sq was metabolized into its glucuronidated form within 24h and was excreted within 72h. MALDI MSI was instrumental in showing that Sq-glucuronide was mainly formed in the gut and slightly in yolk syncytial layer, and provided valuable insights into xenobiotics elimination in zebrafish larvae. This study indicates that Sq has a good safety profile and merits further investigations in other disease models. In addition, the optimized MALDI MSI protocol provided here can be widely applied to study distribution and metabolic fate of other structurally related molecules.

Establishing and Validating Cellular Functional Target Engagement Assay for Selective IRAK4 Inhibitor Discovery.

One of the main reasons for the lack of drug efficacy in late-stage clinical trials is the lack of specific and selective target engagement. To increase the likelihood of success of new therapeutics, one approach is to conduct proximal target engagement testing during the early phases of preclinical drug discovery. To identify and optimize selective IRAK4 inhibitors, a kinase that has been implicated in multiple inflammatory and autoimmune diseases, we established an electrochemiluminescence (ECL)-based cellular endogenous IRAK1 activation assay as the most proximal functional evaluation of IRAK4 engagement to support structure-activity relationship (SAR) studies. Since IRAK1 activation is dependent on both the IRAK4 scaffolding function in Myddosome formation and IRAK4 kinase activity for signal transduction, this assay potentially captures inhibitors with different mechanisms of action. Data from this IRAK1 assay with compounds representing different structural classes showed statistically significant correlations when compared with results from both IRAK4 biochemical kinase activity and functional peripheral blood mononuclear cell (PBMC)-derived tumor necrosis factor α (TNFα) secretion assays, validating the biological relevancy of the IRAK1 target engagement as a biomarker of the IRAK4 activity. Plate uniformity and potency reproducibility evaluations demonstrated that this assay is amenable to high throughput. Using Bland-Altman assay agreement analysis, we demonstrated that incorporating such proximal pharmacological assessment of cellular target engagement to an in vitro screening funnel for SAR studies can prevent compound optimization toward off-target activity.

Statins as Potential Therapeutics for Esophageal Cancer.

Esophageal cancer is a malignancy that has a poor prognosis, which is mainly due to patients presenting once the cancer is in the advances stages. Chemotherapy has been the mainstay for treating esophageal cancer. However, these agents are not consistently effective and fail to differentiate between the different subtypes of esophageal cancers. Targeted therapies have slowly been introduced into the clinical setting, and initial results seem to be promising. Nevertheless, these medications are not universally cheap and also have non-negligible side effects. Therefore, identifying other classes of drugs which could possess anti-esophageal cancer properties is appealing. In addition to expediting the research and development phases of drug discovery, these agents will have known side effect profiles. Statins are a class of cholesterol-lowering medications that have been prescribed for decades. There is a growing body of literature that has shown the anticancer properties of statins in the setting of various malignancies. Herein, we summarize and assimilate the current evidence pertaining to the potential anti-esophageal cancer benefits of statins. We also discuss the limitations of the published studies and consider the future role statins can play in treating patients with esophageal cancers.

Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.

Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding affinity. Graph convolutional neural networks reduce the computational time and resources that are normally required by the traditional convolutional neural network models. In this technique, the structure of a protein-ligand complex is represented as a graph of multiple adjacency matrices whose entries are affected by distances, and a feature matrix that describes the molecular properties of the atoms. We evaluated the predictive power of GraphBAR for protein-ligand binding affinities by using PDBbind datasets and proved the efficiency of the graph convolution. Given the computational efficiency of graph convolutional neural networks, we also performed data augmentation to improve the model performance. We found that data augmentation with docking simulation data could improve the prediction accuracy although the improvement seems not to be significant. The high prediction performance and speed of GraphBAR suggest that such networks can serve as valuable tools in drug discovery.

Computational prediction of photosensitizers’ toxicity

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, and to assess potential mutagenic impurities in development and degradants as part of life-cycle management. In this study, for the first time, in silico approaches were used to analyze the possible dark toxicity of photosensitive systems based on chlorin e6 and assessed possible toxicity of these compositions. By applying quantitative structure-activity relationship models (QSARs) and modeling adverse outcome pathways (AOPs), a potential toxic effect of water-soluble (chlorin e6 and chlorin e6 aminoamid) and hydrophobic (tetraphenylporphyrin) photosensitizers (PS) was predicted. Particularly, PSs’ protein binding ability, reactivity to form peptide adducts, glutathione conjugation, activity in dendritic cells, and gene expression activity in keratinocytes were explored. Using a metabolism simulator, possible PS metabolites were predicted and their potential toxicity was assessed as well. It was shown that all tested porphyrin PS and their predicted metabolites possess low activity in the mentioned processes and therefore are unable to cause significant adverse toxic effects under dark conditions.

Establishment of inhibitor screening and validation system for tryptophanyl tRNA synthetase using surface plasmon resonance

With the increase in throughput and sensitivity, biophysical technology has become a major component of the early drug discovery phase. Surface plasmon resonance technology (SPR) is one of the most widely used biophysical technologies. It has the advantages of circumventing labeling, molecular weight limitations, and neglect of low affinity interactions, etc., and provides a robust platform for hit to lead discovery and optimization. Here, we successfully established a reliable and repeatable tryptophanyl tRNA synthetase (TrpRS) SPR high-throughput screening and validation system by optimizing the TrpRS tag, TrpRS immobilization methodology, and the buffer conditions. When TrpRS was immobilized on Streptavidin (SA) sensor chip, the substrate competitive inhibitor indolmycin exhibited the best binding affinity in HBS-P (10 mM HEPES, 150 mM NaCl, 0.05% surfactant P-20, pH 7.4), 1 mM ATP and MgCl2, with a KD (dissociation equilibrium constant) value of 0.6 ± 0.1 μM. The Z-factor values determined in the screening assays were all larger than 0.9. We hope that our proposed research ideas and methods may provide a scientific basis for establishing SPR analysis of other drug targets, accelerate the discovery and optimization of target lead compounds, and assist the clinical application of next-generation drugs.

The emergence of imaging mass spectrometry in drug discovery and development: Making a difference by driving decision making.

The pharmaceutical industry is a dynamic, science‐driven business constantly under pressure to innovate and morph into a higher performing organization. Innovations can include the implementation of new technologies, adopting new scientific methods, changing the decision‐making process, compressing timelines, or making changes to the organizational structure. The drivers for the constant focus on performance improvement are the high cost of R&D as well as the lengthy timelines required to deliver new medicines for unmet needs. Successful innovations are measured against both the quality and quantity of potential new medicines in the pipeline and the delivery to patients. In this special feature article, we share our collective experience implementing matrix‐assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) technology as an innovative approach to better understand the tissue biodistribution of drugs in the early phases of drug discovery to establish pharmacokinetic‐pharmacodynamic (PK‐PD) relationships, as well as in the development phase to understand pharmacology, toxicology, and disease pathogenesis. In our experience, successful implementation of MALDI IMS in support of therapeutic programs can be measured by the impact IMS studies have on driving decision making in pipeline progression. This provides a direct quantifiable measurement of the return to the organization for the investment in IMS. We have included discussion not only on the technical merits of IMS study conduct but also the key elements of setting study objectives, building collaborations, data integration into the medicine progression milestones, and potential pitfalls when trying to establish IMS in the pharmaceutical arena. We categorized IMS study types into five groups that parallel pipeline progression from the earliest phases of discovery to late stages of preclinical development. We conclude the article with some perspectives on how we see MALDI IMS maintaining relevance and becoming further embedded as an essential tool in the constantly changing environment of the pharmaceutical industry.

Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery.

The optimal pharmacokinetic (PK) required for a drug candidate to elicit efficacy is highly dependent on the targeted pharmacology, a relationship that is often not well characterized during early phases of drug discovery. Generic assumptions around PK and potency risk misguiding screening and compound design toward nonoptimal absorption, distribution, metabolism, and excretion (ADME) or molecular properties and ultimately may increase attrition as well as hit-to-lead and lead optimization timelines. The present work introduces model-based target pharmacology assessment (mTPA), a computational approach combining physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling, sensitivity analysis, and machine learning (ML) to elucidate the optimal combination of PK, potency, and ADME specific for the targeted pharmacology. Examples using frequently encountered PK/PD relationships are presented to illustrate its application, and the utility and benefits of deploying such an approach to guide early discovery efforts are discussed.

Innovation in bioanalytical strategies and in vitro drug-drug interaction study approaches in drug discovery.

Failure to evaluate actual toxicities of investigational molecules in drug discovery is majorly due to inadequate evaluation of their pharmacokinetics. Limitation of conventional drug metabolism profiling procedure demands advancement of existing approaches. Various techniques such as 3D cell culture system, bio microfluidic OoC model, sandwich culture model is in pipeline to be employed at their full potential in drug discovery phase. Although they outweigh the conventional techniques in various aspects, a more detailed exploration of applicability in terms of automation and high throughput analysis is required. This review extensively discusses various ongoing innovations in bioanalytical techniques. The review also proposed various scientific strategies to be adopted for prior assessment of interaction possibilities in translational drug discovery research.

Drug discovery: Insights from the invertebrate Caenorhabditis elegans.

Therapeutic drug development is a long, expensive, and complex process that usually takes 12–15 years. In the early phases of drug discovery, in particular, there is a growing need for animal models that ensure the reduction in both cost and time. Caenorhabditis elegans has been traditionally used to address fundamental aspects of key biological processes, such as apoptosis, aging, and gene expression regulation. During the last decade, with the advent of large‐scale platforms for screenings, this invertebrate has also emerged as an essential tool in the pharmaceutical research industry to identify novel drugs and drug targets. In this review, we discuss the reasons why C. elegans has been positioned as an outstanding cost‐effective option for drug discovery, highlighting both the advantages and drawbacks of this model. Particular attention is paid to the suitability of this nematode in large‐scale genetic and pharmacological screenings. High‐throughput screenings in C. elegans have indeed contributed to the breakthrough of a wide variety of candidate compounds involved in extensive fields including neurodegeneration, pathogen infections and metabolic disorders. The versatility of this nematode, which enables its instrumentation as a model of human diseases, is another attribute also herein underscored. As illustrative examples, we discuss the utility of C. elegans models of both human neurodegenerative diseases and parasitic nematodes in the drug discovery industry. Summing up, this review aims to demonstrate the impact of C. elegans models on the drug discovery pipeline.

Molecular Modeling Studies on Cytochrome P450-mediated Drug Metabolism.

Drug metabolism studies play a critical role in the optimization of the therapeutic efficacy of newer drug candidates. Many drug candidates and drugs were withdrawn from the pre-clinical/clinical stage or market due to the poor metabolic profiles. The poor metabolic profiles may make the clinical candidates/drugs inactive or toxic. Therefore, it is necessary to optimize the metabolic profiles at the initial phase of drug discovery and development processes. Recently, molecular modeling approaches were found to be useful in the optimization of metabolic profiles of clinical candidates. The molecular modeling approaches were employed in the identification of various metabolic profiles. In the present study, the current status of the research work on drug metabolism based on molecular modeling methodologies has been reviewed. The basics of drug metabolism and its importance in the physiological process of the human body have been described. Moreover, the involvement of molecular modeling approaches like pharmacophore- based modeling, QSAR, molecular docking, virtual screening, quantum chemical analysis, molecular dynamics, etc., in predicting metabolic profiles of therapeutic agents is analyzed. The present review provides computational insights in the prediction of substrate specificity, metabolic activity, SOM, metabolites, toxicity, etc., on cytochrome P450-mediated drug metabolism. The study may help the researchers to design novel drug candidates for the various classes of therapeutic targets with efficient metabolic profiles.