Designing better drugs: predicting cytochrome P450 metabolism

Deuterium Editing of Small Molecules: A Case Study on Antitumor Activity of 1,4-Benzodiazepine-2,5-dione Derivatives

Substituting hydrogen with deuterium in drug molecules is an appealing bioisosteric strategy for the generation of novel chemical entities in drug development. Optimizing lead compounds through deuteration has proven to be challenging and unpredictable, particularly for compounds with multiple metabolic sites. This study presents the pioneering achievement of substituting up to 19 hydrogen atoms with deuterium on 1,4-benzodiazepine-2,5-dione derivatives, shedding light on the structure-metabolism relationship and the impact of multiple deuterations on drug-like properties. Notably, the deuterated compound 3f exhibited remarkable antitumor activity in vivo and demonstrated favorable drug-like properties as a drug candidate.

Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Fusion of Multiple Spectra for Investigating Chemical Bonding Properties via Machine Learning

Chemical bonding properties are crucial to understanding the chemical behavior of molecules. Spectroscopy is a versatile technical tool to study various microscopic properties, but its interpretation suffers from human biases and the loss of high-dimensional information. Here, we present a machine learning approach to predict diverse bonding properties, including the bond dissociation energy, bond length, and α-C connectivity of hydroxyls in organic molecules, by fusing multiple spectra with different physical mechanisms. Combining nuclear magnetic resonance and vibrational spectroscopy exhibits higher prediction accuracy than what they did separately. On the hold-out test data set, the models achieve a mean absolute error of 1.243 kcal/mol and 1.041 × 10-4 Å for BDE and bond length and an accuracy of 95.09% for hydroxyl α-C connectivity. Our models demonstrate strong extrapolation capabilities when they are transferred to different molecules, external electric fields, and solvation environments. These end-to-end models pave the way to investigating chemical bonding properties by using spectroscopic observables.

Discovery of Chalcone-Based Hybrid Structures as High Affinity and Site-Specific Inhibitors against SARS-CoV-2: A Comprehensive Structural Analysis Based on Various Host-Based and Viral Targets

Previous studies indicated that natural-based chalcones have significant inhibitory effects on the coronavirus enzymes 3CLpro and PLpro as well as modulation of some host-based antiviral targets (HBATs). In this study, a comprehensive computational and structural study was performed to investigate the affinity of our compound library consisting of 757 chalcone-based structures (CHA-1 to CHA-757) for inhibiting the 3CLpro and PLpro enzymes and against twelve selected host-based targets. Our results indicated that CHA-12 (VUF 4819) is the most potent and multi-target inhibitor in our chemical library over all viral and host-based targets. Correspondingly, CHA-384 and its congeners containing ureide moieties were found to be potent and selective 3CLpro inhibitors, and benzotriazole moiety in CHA-37 was found to be a main fragment for inhibiting the 3CLpro and PLpro. Surprisingly, our results indicate that the ureide and sulfonamide moieties are integral fragments for the optimum 3CLpro inhibition while occupying the S1 and S3 subsites, which is fully consistent with recent reports on the site-specific 3CLpro inhibitors. Finding the multi-target inhibitor CHA-12, previously reported as an LTD4 antagonist for the treatment of inflammatory pulmonary diseases, prompted us to suggest it as a concomitant agent for relieving respiratory symptoms and suppressing COVID-19 infection.

Strategies for Assessing Acceptable Intakes for Novel N-Nitrosamines Derived from Active Pharmaceutical Ingredients

The detection of N-nitrosamines, derived from solvents and reagents and, on occasion, the active pharmaceutical ingredient (API) at higher than acceptable levels in drug products, has led regulators to request a detailed review for their presence in all medicinal products. In the absence of rodent carcinogenicity data for novel N-nitrosamines derived from amine-containing APIs, a conservative class limit of 18 ng/day (based on the most carcinogenic N-nitrosamines) or the derivation of acceptable intakes (AIs) using structurally related surrogates with robust rodent carcinogenicity data is recommended. The guidance has implications for the pharmaceutical industry given the vast number of marketed amine-containing drugs. In this perspective, the rate-limiting step in N-nitrosamine carcinogenicity, involving cytochrome P450-mediated α-carbon hydroxylation to yield DNA-reactive diazonium or carbonium ion intermediates, is discussed with reference to the selection of read-across analogs to derive AIs. Risk-mitigation strategies for managing putative N-nitrosamines in the preclinical discovery setting are also presented.

Cytochrome P450 Enzymes and Drug Metabolism in Humans

Human cytochrome P450 (CYP) enzymes, as membrane-bound hemoproteins, play important roles in the detoxification of drugs, cellular metabolism, and homeostasis. In humans, almost 80% of oxidative metabolism and approximately 50% of the overall elimination of common clinical drugs can be attributed to one or more of the various CYPs, from the CYP families 1–3. In addition to the basic metabolic effects for elimination, CYPs are also capable of affecting drug responses by influencing drug action, safety, bioavailability, and drug resistance through metabolism, in both metabolic organs and local sites of action. Structures of CYPs have recently provided new insights into both understanding the mechanisms of drug metabolism and exploiting CYPs as drug targets. Genetic polymorphisms and epigenetic changes in CYP genes and environmental factors may be responsible for interethnic and interindividual variations in the therapeutic efficacy of drugs. In this review, we summarize and highlight the structural knowledge about CYPs and the major CYPs in drug metabolism. Additionally, genetic and epigenetic factors, as well as several intrinsic and extrinsic factors that contribute to interindividual variation in drug response are also reviewed, to reveal the multifarious and important roles of CYP-mediated metabolism and elimination in drug therapy.

Non-cytochrome P450 enzymes involved in the oxidative metabolism of xenobiotics: Focus on the regulation of gene expression and enzyme activity

Oxidative metabolism is one of the major biotransformation reactions that regulates the exposure of xenobiotics and their metabolites in the circulatory system and local tissues and organs, and influences their efficacy and toxicity. Although cytochrome (CY)P450s play critical roles in the oxidative reaction, extensive CYP450-independent oxidative metabolism also occurs in some xenobiotics, such as aldehyde oxidase, xanthine oxidoreductase, flavin-containing monooxygenase, monoamine oxidase, alcohol dehydrogenase, or aldehyde dehydrogenase-dependent oxidative metabolism. Drugs form a large portion of xenobiotics and are the primary target of this review. The common reaction mechanisms and roles of non-CYP450 enzymes in metabolism, factors affecting the expression and activity of non-CYP450 enzymes in terms of inhibition, induction, regulation, and species differences in pharmaceutical research and development have been summarized. These non-CYP450 enzymes are detoxifying enzymes, although sometimes they mediate severe toxicity. Synthetic or natural chemicals serve as inhibitors for these non-CYP450 enzymes. However, pharmacokinetic-based drug interactions through these inhibitors have rarely been reported in vivo. Although multiple mechanisms participate in the basal expression and regulation of non-CYP450 enzymes, only a limited number of inducers upregulate their expression. Therefore, these enzymes are considered non-inducible or less inducible. Overall, this review focuses on the potential xenobiotic factors that contribute to variations in gene expression levels and the activities of non-CYP450 enzymes.

Toxicity and remediation of pharmaceuticals and pesticides using metal oxides and carbon nanomaterials

The worldwide development of agriculture and industry has resulted in contamination of water bodies by pharmaceuticals, pesticides and other xenobiotics. Even at trace levels of few micrograms per liter in waters, these contaminants induce public health and environmental issues, thus calling for efficient removal methods such as adsorption. Recent adsorption techniques for wastewater treatment involve metal oxide compounds, e.g. Fe2O3, ZnO, Al2O3 and ZnO-MgO, and carbon-based materials such as graphene oxide, activated carbon, carbon nanotubes, and carbon/graphene quantum dots. Here, the small size of metal oxides and the presence various functional groups has allowed higher adsorption efficiencies. Moreover, carbon-based adsorbents exhibit unique properties such as high surface area, high porosity, easy functionalization, low price, and high surface reactivity. Here we review the cytotoxic effects of pharmaceutical drugs and pesticides in terms of human risk and ecotoxicology. We also present remediation techniques involving adsorption on metal oxides and carbon-based materials.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

The intrinsic metabolic clearance rate (Cl_int) and the fraction of the chemical unbound in plasma (f_up) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review

Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.

Enzyme Kinetics, Pharmacokinetics, and Inhibition of Aldehyde Oxidase

Aldehyde oxidase (AO) has emerged as an important drug metabolizing enzyme over the last decade. Several compounds have failed in the clinic because the clearance or toxicity was underestimated by preclinical species. Human AO is much more active than rodent AO, and dogs do not have functional AO. Metabolic products from AO-catalyzed oxidation are generally nonreactive and often they have much lower solubility. AO metabolism is not limited to oxidation as AO can also catalyze reduction of oxygen and nitrite. Reduction of oxygen leads to the reactive oxygen species (ROS) superoxide radical anion and hydrogen peroxide. Reduction of nitrite leads to the formation of nitric oxide with potential pharmacological implications. AO is also reported to catalyze the reductive metabolism of nitro-compounds, N-oxides, sulfoxides, isoxazoles, isothiazoles, nitrite, and hydroxamic acids. These reductive transformations may cause toxicity due to the formation of reactive metabolites. Moreover, the inhibition kinetics are complex, and multiple probe substrates should be used when assessing the potential for DDIs. Finally, AO appears to be amenable to computational predictions of both regioselectivity and rates of reaction, which holds promise for virtual screening.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol^-1 (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation.

Molecular Dynamics Simulation Framework to Probe the Binding Hypothesis of CYP3A4 Inhibitors

The Cytochrome P450 family of heme-containing proteins plays a major role in catalyzing phase I metabolic reactions, and the CYP3A4 subtype is responsible for the metabolism of many currently marketed drugs. Additionally, CYP3A4 has an inherent affinity for a broad spectrum of structurally diverse chemical entities, often leading to drug-drug interactions mediated by the inhibition or induction of the metabolic enzyme. The current study explores the binding of selected highly efficient CYP3A4 inhibitors by docking and molecular dynamics (MD) simulation protocols and their binding free energy calculated using the WaterSwap method. The results indicate the importance of binding pocket residues including Phe57, Arg105, Arg106, Ser119, Arg212, Phe213, Thr309, Ser312, Ala370, Arg372, Glu374, Gly481 and Leu483 for interaction with CYP3A4 inhibitors. The residue-wise decomposition of the binding free energy from the WaterSwap method revealed the importance of binding site residues Arg106 and Arg372 in the stabilization of all the selected CYP3A4-inhibitor complexes. The WaterSwap binding energies were further complemented with the MM(GB/PB)SA results and it was observed that the binding energies calculated by both methods do not differ significantly. Overall, our results could guide towards the use of multiple computational approaches to achieve a better understanding of CYP3A4 inhibition, subsequently leading to the design of highly specific and efficient new chemical entities with suitable ADMETox properties and reduced side effects.

Gancao (Glycyrrhizae Radix) provides the main contribution to Shaoyao-Gancao decoction on enhancements of CYP3A4 and MDR1 expression via pregnane X receptor pathway in vitro

BACKGROUND

Chinese herbal formula Shaoyao Gancao decoction (SGD) is often used as an adjuvant with chemotherapeutic agents to treat cancer. Due to the herb-drug interactions, the alternations of drug metabolic enzyme and drug transporters induced by SGD deserve to be explored. We aimed to investigate the effect of SGD on the pregnane X receptor (PXR)-mediated transcriptional regulation of cytochrome P450 3A4 (CYP3A4) and drug transporter multidrug resistance protein 1 (MDR1) in vitro. Besides, we assessed the contribution of constituent herbs to SGD on the regulation of CYP3A4 and MDR1.

METHODS

The dual luciferase reporter gene system containing the hPXR expression plasmid and the reporter gene plasmid of CYP3A4 or MDR1 was co-transfected to HepG2 and Caco2 cells. Luciferase activities were determined using a Dual-luciferase reporter assay kit. The gene expression of CYP3A4 and MDR1 in the hPXR-transfected LS174T cells were assessed by real-time qPCR. Finally, the contribution of constituent herbs from SGD was evaluated.

RESULTS

SGD, Shaoyao and Gancao concentration-dependently increased promoter activities of CYP3A4 and MDR1 in vitro. Moreover, SGD, Shaoyao and Gancao up-regulated CYP3A4 and MDR1 mRNA in hPXR-transfected LS174T cells. As the herbal constituent of SGD, Gancao possesses significantly higher levels of metabolic enzyme and drug transporters compared with Shaoyao.

CONCLUSION

SGD tends to enhance CYP3A4 and MDR1 expression via PXR pathway, especially Gancao provides the main contribution. This study highlights a potential in vitro mechanism for SGD on the regulation of drug metabolic enzymes and drug transporters.

Molecular cloning and 3D model of first cytochrome P450 from CYP3A subfamily in saltwater crocodile (Crocodylus porosus)

Cytochrome P450s (CYPs) play critical role in oxidative metabolism of numerous xenobiotics and endogenous compounds. The first CYP3A subfamily member in saltwater crocodile has been cloned and modelled for three-dimensional (3D) structure. The full-length cDNA was obtained employing reverse transcription polymerase chain reaction (RT-PCR) strategy and rapid amplification of cDNA ends (RACE). The cDNA sequence of 1659 nucleotides includes 132 nucleotides from 5' untranslated region (UTR), an open reading frame of 1527 nucleotides encoding 509 amino acids designated as CYP3A163. The alignment of CYP3A163 sequence with CYP3A subfamily across the lineages exhibit the loss of 1 residue in birds and 7 residues in mammals in comparison to reptiles suggesting the adaptation processes during evolution. The amino acid identity of CYP3A163 with Alligator mississippiensis CYP3A77 and Homo sapiens CYP3A4 is 91% and 62% respectively. The 3D structure of CYP3A163 modelled using human CYP3A4 structure as a template with Phyre² software, represents high similarity with its functionally important motifs and catalytic domain. Both sequence and structure of CYP3A163 display the common and conserved features of CYP3A subfamily. Overall, this study provides primary molecular and structural data of CYP3A163 required to investigate the xenobiotic metabolism in saltwater crocodiles.

Investigation of substrate recognition for cytochrome P450 1A2 mediated by water molecules using docking and molecular dynamics simulations

The role of water molecules in the active site of cytochrome P450 1A2 (CYP1A2) was investigated using an explicit water model to simulate biological environments. Moreover, differences in ligand recognition between the inhibitor α-naphthoflavone (ANF) and the substrate 7-ethoxyresorufin (7ER) in the CYP1A2 complex were examined. More than 200-ns molecular dynamics (MD) simulations were performed for each complex structure of CYP1A2. In the complex structure with 7ER obtained after MD simulation, some water molecules existed in the active site and formed hydrogen bonds between 7ER and some residues. However, in the complex structure with ANF, the hydrogen bond network differed. These results suggest that CYP1A2 requires water molecules in its active site for substrate recognition. The observed differences in the hydrogen bond network in the complex with ANF or 7ER may be due to the fact that ANF is an inhibitor.

Homology model, molecular dynamics simulation and novel pyrazole analogs design of Candida albicans CYP450 lanosterol 14 α-demethylase, a target enzyme for antifungal therapy

Candida albicans infections and their resistance to clinically approved azole drugs are major concerns for human. The azole antifungal drugs inhibit the ergosterol synthesis by targeting lanosterol 14α-demethylase of cytochrome P450 family. The lack of high-resolution structural information of fungal pathogens has been a barrier for the design of modified azole drugs. Thus, a preliminary theoretical molecular dynamic study is carried out to develop and validate a simple homologous model using crystallographic structure of the lanosterol 14α-demethylase of Mycobacterium tuberculosis (PDB ID-1EA1) in which the active site residues are substituted with that of C. albicans (taxid 5476). Further, novel designed pyrazole analogs (SGS1-16) docked on chimeric 1EA1 and revealed that SGS-16 show good binding affinity through non-bonding interaction with the heme, which is different from the leading azole antifungals. The ADME-T results showed these analogs can be further explored in design of more safe and effective antifungal agents.

In Vitro Effects of Concomitant Use of Herbal Preparations on Cytochrome P450s Involved in Clozapine Metabolism

Herbal supplements are increasingly used in psychiatric practice. Our epidemiological study has identified several herbal preparations associated with adverse outcomes of antipsychotic therapy. In this study, we evaluated the in vitro effects of four herbal preparations—Radix Rehmanniae (RR), Fructus Schisandrae (FS), Radix Bupleuri (RB) and Fructus Gardeniae (FG)—on cytochrome P450s (CYPs) involved in the metabolism of clozapine in human liver microsomes (HLMs) and recombinant human cytochrome P450 enzymes (rCYPs). N-desmethylclozapine and clozapine N-oxide, two major metabolites of clozapine, were measured using high-performance liquid chromatography (HPLC). FG, RR and RB showed negligible inhibitory effects in both in vitro systems, with estimated half-maximal inhibitory concentrations (IC₅₀) and apparent inhibitory constant values (K_i) greater than 1 mg/mL (raw material), suggesting that minimal metabolic interaction occurs when these preparations are used concomitantly with clozapine. The FS extract affected CYP activity with varying potency; its effect on CYP 3A4-catalyzed clozapine oxidation was relatively strong (K_i: 0.11 mg/mL). Overall, the weak-to-moderate inhibitory effect of FS on in vitro clozapine metabolism indicated its potential role in herb-drug interaction in practice.

The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism

Although the importance of human genetic polymorphisms in therapeutic outcomes is well established, the role of our 'second genome' (the microbiome) has been largely overlooked. In this Review, we highlight recent studies that have shed light on the mechanisms that link the human gut microbiome to the efficacy and toxicity of xenobiotics, including drugs, dietary compounds and environmental toxins. Continued progress in this area could enable more precise tools for predicting patient responses and for the development of a new generation of therapeutics based on, or targeted at, the gut microbiome. Indeed, the admirable goal of precision medicine may require us to first understand the microbial pharmacists within.

Comparative proteomics among cytochrome p450 family 1 for differential substrate specificity

Apart from playing key roles in drug metabolism and adverse drug-drug interactions, CYPs are potential drug targets to treat a variety of diseases. The intervention of over expression of P450 1A1 (CYP1A1) in tumor cells is identified as a novel strategy for anticancer therapy. We investigated three isoforms of CYP1 family (CYP1A1, CYP1A2, and CYP1B1) for their substrate specificity. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics. This can help in design of new antitumor molecule specifically metabolized by CYP1A1 to mediate their antitumor activity. In the present study, we carried out the comparative protein structure analysis of the three isoforms. Sequence alignment, root mean square deviation (RMSD) analysis, B-factor analysis was performed to give a better understanding of the macromolecular features involved in substrate specificity and to understand the interplay between protein dynamics and functions which will have important implications on rational design of anticancer drugs. We identified the differences in amino acid residues among the three isoforms of CYP1 family, which may account for differential substrate specificity. Six putative substrate recognition sequences are characterized along with the regions they form in the protein structure. Further the RMSD and B-factor analysis provides the information about the identified residues having the maximum RMSD and B-factor deviations.

A Combined Molecular Docking/Dynamics Approach to Probe the Binding Mode of Cancer Drugs with Cytochrome P450 3A4

Cytarabine, daunorubicin, doxorubicin and vincristine are clinically used for combinatorial therapies of cancers in different combinations. However, the knowledge about the interaction of these drugs with the metabolizing enzyme cytochrome P450 is limited. Therefore, we utilized computational methods to predict and assess the drug-binding modes. In this study, we performed docking, MD simulations and free energy landscape analysis to understand the drug-enzyme interactions, protein domain motions and the most populated free energy minimum conformations of the docked protein-drug complexes, respectively. The outcome of docking and MD simulations predicted the productive, as well as the non-productive binding modes of the selected drugs. Based on these interaction studies, we observed that S119, R212 and R372 are the major drug-binding residues in CYP3A4. The molecular mechanics Poisson–Boltzmann surface area analysis revealed the dominance of hydrophobic forces in the CYP3A4-drug association. Further analyses predicted the residues that may contain favorable drug-specific interactions. The probable binding modes of the cancer drugs from this study may extend the knowledge of the protein-drug interaction and pave the way to design analogs with reduced toxicity. In addition, they also provide valuable insights into the metabolism of the cancer drugs.

Variability in bioavailability of small molecular tyrosine kinase inhibitors

Small molecular tyrosine kinase inhibitors (smTKIs) are in the centre of the very quickly expanding area of personalized chemotherapy and oral applicability thereof. The number of drugs in this class is rapidly growing, with twenty current approvals by both the European Medicines Agency (EMA) and the Food and Drug Administration (FDA). The drugs are, however, generally characterized by a poor oral, and thus variable, bioavailability. This results in significant variation in plasma levels and exposure. The cause is a complex interplay of factors, including poor aqueous solubility, issued permeability, membrane transport and enzymatic metabolism. Additionally, food and drug-drug interactions can play a significant role. The issues related with an impaired bioavailability generally receive little attention. To the best of our knowledge, this article is the first to provide an overview of the factors that determine the bioavailability of the smTKIs.

Recent progress on structural bioinformatics research of cytochrome P450 and its impact on drug discovery

Cytochrome P450 is predominantly responsible for human drug metabolism, which is of critical importance for drug discovery and development. Structural bioinformatics focuses on analysis and prediction of three-dimentional structure of biological macromolecules and elucidation of structure-function relationship as well as identification of important binding interactions. Rapid advancement of structural bioinformatics has been made over the last decade. With more information available for CYP structures, the methods of structural bioinformatics may be used in the CYP field. In this review, we demonstrate three previous studies on CYP using the methods of structural bioinformatics, including the investigation of reasons for decrease of enzymatic activity of CYP1A2 caused by a peripheral mutation, the construction of a pharmacophore model specific to active site of CYP1A2 and the prediction of the functional consequences of single residue mutation in CYP. By illustrating these studies we attempt to show the potential role of structural bioinformatics in CYP research and help better understanding the importance of structural bioinformatics in drug designing.

Structural and energetic analysis to provide insight residues of CYP2C9, 2C11 and 2E1 involved in valproic acid dehydrogenation selectivity

Docking and molecular dynamics (MD) simulation have been two computational techniques used to gain insight about the substrate orientation within protein active sites, allowing to identify potential residues involved in the binding and catalytic mechanisms. In this study, both methods were combined to predict the regioselectivity in the binding mode of valproic acid (VPA) on three cytochrome P-450 (CYP) isoforms CYP2C9, CYP2C11, and CYP2E1, which are involved in the biotransformation of VPA yielding reactive hepatotoxic intermediate 2-n-propyl-4-pentenoic acid (4nVPA). There are experimental data about hydrogen atom abstraction of the C4-position of VPA to yield 4nVPA, however, there are not structural evidence about the binding mode of VPA and 4nVPA on CYPs. Therefore, the complexes between these CYP isoforms and VPA or 4nVPA were studied to explore their differences in binding and energetic stabilization. Docking results showed that VPA and 4nVPA are coupled into CYPs binding site in a similar conformation, but it does not explain the VPA hydrogen atom abstraction. On the other hand, MD simulations showed a set of energetic states that reorient VPA at the first ns, then making it susceptible to a dehydrogenation reaction. For 4nVPA, multiple binding modes were observed in which the different states could favor either undergo other reaction mechanism or ligand expulsion from the binding site. Otherwise, the energetic and entropic contribution point out a similar behavior for the three CYP complexes, showing as expected a more energetically favorable binding free energy for the complexes between CYPs and VPA than with 4nVPA.

Structure-based ligand design to overcome CYP inhibition in drug discovery projects

Cytochrome P450 (CYP) enzymes are key players in xenobiotic metabolism, and inhibition of CYPs can therefore result in unwanted drug-drug interactions. Within drug discovery, CYP inhibition can cause delays in the progression of candidate drugs, or even premature closure of projects. During the past decade, a massive effort in the pharmaceutical industry and academic research has produced a wealth of structural information in the CYP field. In this short review, we will describe how structure-based approaches can be used in the pharmaceutical industry to work away from CYP inhibition, with a focus on the opportunities and challenges. We will show two examples from our own work where structural information on CYP2C9 and CYP3A4 inhibitor complexes have been successfully exploited in ongoing drug discovery projects.

Imidazolylchromanones containing non-benzylic oxime ethers: synthesis and molecular modeling study of new azole antifungals selective against Cryptococcus gattii

A series of imidazolylchromanone oximes containing phenoxyethyl ether moiety, as found in omoconazole, were synthesized and evaluated against yeasts (Candida albicans and Cryptococcus gattii) and filamentous fungi (Aspergillus fumigatus and Exophiala dermatitidis). Although the title compounds showed marginal activity against filamentous fungi but all of them exhibited potent activity against C. gattii (MIC values ≤4 μg/mL). Among them, (3-chlorophenoxy)ethyl analog 7c with MIC value of 0.5 μg/mL was the most potent compound. Further molecular docking studies provided a better insight into the binding of designed compounds within the homology modeled active site of CnCYP51 (Cryptococcus CYP51-14α-demethylase).

A simple litmus test for aldehyde oxidase metabolism of heteroarenes

The bioavailability of aromatic azaheterocyclic drugs can be affected by the activity of aldehyde oxidase (AO). Susceptibility to AO metabolism is difficult to predict computationally and can be complicated in vivo by differences between species. Here we report the use of bis(((difluoromethyl)sulfinyl)oxy)zinc (DFMS) as a source of CF₂H radical for a rapid and inexpensive chemical “litmus test” for the early identification of heteroaromatic drug candidates that have a high probability of metabolism by AO.

Correlating structure and function of drug-metabolizing enzymes: progress and ongoing challenges

This report summarizes a symposium sponsored by the American Society for Pharmacology and Experimental Therapeutics at Experimental Biology held April 20-24 in Boston, MA. Presentations discussed the status of cytochrome P450 (P450) knowledge, emphasizing advances and challenges in relating structure with function and in applying this information to drug design. First, at least one structure of most major human drug-metabolizing P450 enzymes is known. However, the flexibility of these active sites can limit the predictive value of one structure for other ligands. A second limitation is our coarse-grain understanding of P450 interactions with membranes, other P450 enzymes, NADPH-cytochrome P450 reductase, and cytochrome b5. Recent work has examined differential P450 interactions with reductase in mixed P450 systems and P450:P450 complexes in reconstituted systems and cells, suggesting another level of functional control. In addition, protein nuclear magnetic resonance is a new approach to probe these protein/protein interactions, identifying interacting b5 and P450 surfaces, showing that b5 and reductase binding are mutually exclusive, and demonstrating ligand modulation of CYP17A1/b5 interactions. One desired outcome is the application of such information to control drug metabolism and/or design selective P450 inhibitors. A final presentation highlighted development of a CYP3A4 inhibitor that slows clearance of human immunodeficiency virus drugs otherwise rapidly metabolized by CYP3A4. Although understanding P450 structure/function relationships is an ongoing challenge, translational advances will benefit from continued integration of existing and new biophysical approaches.

Structural diversity of eukaryotic membrane cytochrome p450s

X-ray crystal structures are available for 29 eukaryotic microsomal, chloroplast, or mitochondrial cytochrome P450s, including two non-monooxygenase P450s. These structures provide a basis for understanding structure-function relations that underlie their distinct catalytic activities. Moreover, structural plasticity has been characterized for individual P450s that aids in understanding substrate binding in P450s that mediate drug clearance.

Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.

Inhibition of cytochrome P450 enzymes and biochemical aspects of mechanism-based inactivation (MBI)

Mechanism-based inactivation (MBI) often involves metabolic bioactivation of the xenobiotic by cytochrome P450s (CYPs) to an electrophilic reactive intermediate and results in quasi-irreversible or irreversible inactivation. Such reactive intermediate can cause quasi-irreversible inhibition through coordination to the ferrous state, Fe(II), of the P450 enzyme forming a tight noncovalent bond leading to the formation of metabolic-intermediate complex (MIC). By contrast, irreversible inactivation is one of the most common causes for the observed drug–drug interaction (DDI) and usually implies the formation of a covalent bond between the metabolite and the enzyme via alkylation of either the heme or the P450 apoprotein. Here we illustrate the important points of the current literature understanding of the mechanisms of inhibition of CYP enzymes with emphasis on general mechanistic aspects of MBI for some drugs/moieties associated with the phenomenon. Additionally, the utility of computational and in silico approaches to address bioactivation issues will be briefly discussed.

Mechanism of the plant cytochrome P450 for herbicide resistance: a modelling study

Plant cytochrome P450 is a key enzyme responsible for the herbicide resistance but the molecular basis of the mechanism is unclear. To understand this, four typical plant P450s and a widely resistant herbicide chlortoluron were analysed by carrying out homology modelling, molecular docking, molecular dynamics simulations and binding free energy analysis. Our results demonstrate that: (i) the putative hydrophobic residues located in the F-helix and polar residues in I-helix are critical in the herbicide resistance; (ii) the binding mode analysis and binding free energy calculation indicate that the distance between catalytic site of chlortoluron and heme of P450, as well as the binding affinity are key elements affecting the resistance for plants. In conclusion, this work provides a new insight into the interactions of plant P450s with herbicide from a molecular level, offering valuable information for the future design of novel effective herbicides which also escape from the P450 metabolism.

INDI: a computational framework for inferring drug interactions and their associated recommendations

INDI is a similarity-based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice.

INDI is a similarity-based drug–drug interaction prediction method, capable of handling both pharmacokinetic and pharmacodynamic interactions.

INDI predicts the severity of the interaction and the Cytochrome P450 isozyme involved in pharmacokinetic interactions.

We show the prevalence of known and predicted drug interactions in drug adverse reports and in chronic medications taken by hospitalized patients.

Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.

Development of in silico filters to predict activation of the pregnane X receptor (PXR) by structurally diverse drug-like molecules

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

Identification and characterization of reactive metabolites in natural products-driven drug discovery

Toxicity of natural products arising from their metabolic biotransformation into reactive chemical intermediates is an important reason for high attrition rates in early drug discovery efforts. Screening promising natural products for their likelihood to form such metabolites is therefore an important step in identifying potential liabilities in the drug development process. However, such screening is complicated by the need to have test methods that are sensitive, reliable, accurate, efficient, and cost-effective enough to allow for routine identification and characterization of the reactive metabolites. These metabolites are typically formed in minute quantities, usually through minor metabolic pathways, and, due to their highly reactive and therefore transient chemical nature, pose considerable analytical challenges in attempts to determine their properties. Understanding the formation of reactive metabolites may be used as the basis for synthetic chemical modification of parent natural products aimed at bypassing such harmful bioactivation. This paper highlights the general principles and protocols commonly used to predict and study the formation of reactive metabolites in vitro and how the data obtained from such studies can be used in the development of safer drugs from natural products.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

The biology of insecticidal activity and resistance

Identifying insecticide resistance mechanisms is paramount for pest insect control, as the understandings that underpin insect control strategies must provide ways of detecting and managing resistance. Insecticide resistance studies rely heavily on detailed biochemical and genetic analyses. Although there have been many successes, there are also many examples of resistance that still challenge us. As a precursor to rational pest insect control, the biology of the insect, within the contexts of insecticide modes of action and insecticide metabolism, must be well understood. It makes sense to initiate this research in the best model insect system, Drosophila melanogaster, and translate these findings and methodologies to other insects. Here we explore the usefulness of the D. melanogaster model in studying metabolic-based insecticide resistances, target-site mediated resistances and identifying novel insecticide targets, whilst highlighting the importance of having a more complete understanding of insect biology for insecticide studies.

From known knowns to known unknowns: predicting in vivo drug metabolites

'It is better to be useful than perfect'. This review attempts to critically cover and assess the currently available approaches and tools to answer the crucial question: Is it possible (and if it is, to what extent is it possible) to predict in vivo metabolites and their abundances on the basis of in vitro and preclinical animal studies? In preclinical drug development, it is possible to produce metabolite patterns from a candidate drug by virtual means (i.e., in silico models), but these are not yet validated. However, they may be useful to cover the potential range of metabolites. In vitro metabolite patterns and apparent relative abundances are produced by various in vitro systems employing tissue preparations (mainly liver) and in most cases using liquid chromatography-mass spectrometry analytical techniques for tentative identification. The pattern of the metabolites produced depends on the enzyme source; the most comprehensive source of drug-metabolizing enzymes is cultured human hepatocytes, followed by liver homogenate fortified with appropriate cofactors. For specific purposes, such as the identification of metabolizing enzyme(s), recombinant enzymes can be used. Metabolite data from animal in vitro and in vivo experiments, despite known species differences, may help pinpoint metabolites that are not apparently produced in in vitro human systems, or suggest alternative experimental approaches. The range of metabolites detected provides clues regarding the enzymes attacking the molecule under study. We also discuss established approaches to identify the major enzymes. The last question, regarding reliability and robustness of metabolite extrapolations from in vitro to in vivo, both qualitatively and quantitatively, cannot be easily answered. There are a number of examples in the literature suggesting that extrapolations are generally useful, but there are only a few systematic and comprehensive studies to validate in vitro-in vivo extrapolations. In conclusion, extrapolation from preclinical metabolite data to the in vivo situation is certainly useful, but it is not known to what extent.