In silico fragment-based drug design

Importance of the field: In silico fragment-based drug design (FBDD) is a relatively new approach inspired by the success of the biophysical fragment-based drug discovery field. Here, we review the progress made by this approach in the last decade and showcase how it complements and expands the capabilities of biophysical FBDD and structure-based drug design to generate diverse, efficient drug candidates.

Areas covered in this review: Advancements in several areas of research that have enabled the development of in silico FBDD and some applications in drug discovery projects are reviewed.

What the reader will gain: The reader is introduced to various computational methods that are used for in silico FBDD, the fragment library composition for this technique, special applications used to identify binding sites on the surface of proteins and how to assess the druggability of these sites. In addition, the reader will gain insight into the proper application of this approach from examples of successful programs.

Take home message: In silico FBDD captures a much larger chemical space than high-throughput screening and biophysical FBDD increasing the probability of developing more diverse, patentable and efficient molecules that can become oral drugs. The application of in silico FBDD holds great promise for historically challenging targets such as protein–protein interactions. Future advances in force fields, scoring functions and automated methods for determining synthetic accessibility will all aid in delivering more successes with in silico FBDD.     

药物转运体和代谢酶在抗生素药动学中的作用

近年来,对体内药物转运体的研究取得了重大进展,越来越多的转运体被发现及研究,其对药物的跨膜转运,具有重要的意义。各种转运体包括摄取转运体和外排转运体对药物的体内过程以及药物相互作用均有着重要影响。研究表明大多数抗生素的体内过程都与转运体和代谢酶有关,因此,归纳总结了转运体和代谢酶在抗生素的药动学和药物相互作用中的最新研究进展,为临床合理用药提供参考。     

Physiologically based pharmacokinetic modeling to predict complex drug–drug interactions: a case study of AZD2327 and its metabolite,competitive and time‐dependent CYP3A inhibitors

4‐{(R)‐(3‐Aminophenyl)[4‐(4‐fluorobenzyl)‐piperazin‐1‐yl]methyl}‐N,N‐diethylbenzamide (AZD2327) is a highly potent and selective agonist of the δ ‐opioid receptor. AZD2327 and N‐deethylated AZD2327 (M1) are substrates of cytochrome P450 3A (CYP3A4) and comprise a complex multiple inhibitory system that causes competitive and time‐dependent inhibition of CYP3A4. The aim of the current work was to develop a physiologically based pharmacokinetic (PBPK) model to predict quantitatively the magnitude of CYP3A4 mediated drug–drug interaction with midazolam as the substrate. Integrating in silico, in vitro and in vivo PK data, a PBPK model was successfully developed to simulate the clinical accumulation of AZD2327 and its primary metabolite. The inhibition of CYP3A4 by AZD2327, using midazolam as a probe drug, was reasonably predicted. The predicted maximum concentration (C_max) and area under the concentration–time curve (AUC) for midazolam were increased by 1.75 and 2.45‐fold, respectively, after multiple dosing of AZD2327, indicating no or low risk for clinically relevant drug–drug interactions (DDI). These results are in agreement with those obtained in a clinical trial with a 1.4 and 1.5‐fold increase in C_max and AUC of midazolam, respectively. In conclusion, this model simulated DDI with less than a two‐fold error, indicating that complex clinical DDI associated with multiple mechanisms, pathways and inhibitors (parent and metabolite) can be predicted using a well‐developed PBPK model. Copyright © 2015 John Wiley & Sons, Ltd.     

Cytochrome P450 1A1-mediated anticancer drug discovery: in silico findings

Introduction: Target-specific drugs may offer fewer side/adverse effects in comparison with other anticancer agents and thus save normal healthy cells to a greater extent. The selective overexpression of cytochrome P450 1A1 (CYP1A1) in tumor cells induces the metabolism of benzothiazole and aminoflavone compounds to their reactive species, which are responsible for DNA adduct formation and cell death. This review encompasses the novelty of CYP1A1 as an anticancer drug target and explores the possible in silico strategies that would be applicable in the discovery and development of future antitumor compounds.

Areas covered: This review highlights the various ligand-based and target-based in silico methodologies that were efficiently used in exploration of CYP1A1 as a novel antitumor target. These methodologies include electronic structure analysis, CoMFA studies, homology modeling, molecular docking, molecular dynamics analysis, pharmacophore mapping and quantitative structure activity relationship (QSAR) studies. It also focuses on the various approaches used in the development of the lysyl amide prodrug of 5F-203 (NSC710305) and dimethanesulfonate salt of 5-aminoflavone (NSC710464) as clinical candidates from their less potent analogues.

Expert opinion: Selective overexpression of CYP1A1 in cancer cells offers tumor-specific drug design to ameliorate the current adverse effects associated with existing antitumor agents. Medicinal chemistry and in vitro driven approaches, in combination with knowledge-based drug design and by using the currently available tools of in silico methodologies, would certainly make it possible to design and develop novel anticancer compounds targeting CYP1A1.     

Entering the era of computationally driven drug development

Abstract

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure–activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.     

Pharmacokinetic behavior presents drug therapy challenges

There are conditions that cause a substantial change in drug clearance to such a degree that how a specific drug is managed to optimize drug response and minimize drug toxicity presents a challenge. This review will focus on recent literature (within the past 5 years) that evaluates pathophysiologic and genetic conditions and drug interactions which can change drug clearance to the magnitude that response is affected. Situations discussed that cause an increase in drug clearance will include: augmented renal clearance in critically ill patients; ultrafast drug metabolism caused by gene duplication; and enzyme induction interactions caused by rifampin. Situations discussed that result in a reduction in clearance will include: multiple organ failure in critically ill, patients with non-functioning CYP2D6 and CYP2C8/9 alleles, and CYP3A4 drug interactions with erythromycin and clarithromycin. In each case evaluated clearance is changed to the magnitude such that managing drug therapy can be difficult.     

High-throughput and in silico screenings in drug discovery

Background: In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones. Objective: Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process. Conclusion: Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.     

Dealing with the complex drug–drug interactions: Towards mechanistic models

Unmanageable severe adverse events caused by drug‐drug interactions (DDIs), leading to market withdrawals or restrictions in the clinical usage, are increasingly avoided with the improvement in our ability to predict such DDIs quantitatively early in drug development. However, significant challenges arise in the evaluation and/or prediction of complex DDIs caused by inhibitor drugs and/or metabolites that affect not one but multiple pathways of drug clearance. This review summarizes the discussion topics at the 2013 AAPS symposium on “Dealing with the complex drug‐drug interactions: towards mechanistic models”. Physiologically based pharmacokinetic (PBPK) models, in combination with the established in vitro‐to‐in vivo extrapolations of intestinal and hepatic disposition, have been successfully applied to predict clinical pharmacokinetics and DDIs, especially for drugs with CYP‐mediated metabolism, and to explain transporter‐mediated and complex DDIs. Although continuous developments are being made towards improved mechanistic prediction of the transporter‐enzyme interplay in the hepatic and intestinal disposition and characterizing the metabolites contribution to DDIs, the prediction of DDIs involving them remains difficult. Regulatory guidelines also recommended use of PBPK modeling for the quantitative prediction and evaluation of DDIs involving multiple perpetrators and metabolites. Such mechanistic modeling approaches culminate to the consensus that modeling is helpful in predicting DDIs or quantitatively rationalizing the clinical findings in complex situations. Furthermore, they provide basis for the prediction and/or understanding the pharmacokinetics in populations like patients with renal impairment, pediatrics, or various ethnic groups where the conduct of clinical studies might not be feasible in early drug development stages and yet some guidance on management of dosage is necessary. Copyright © 2014 John Wiley & Sons, Ltd.     

Retrospective use of PBPK modelling to understand a clinical drug–drug interaction between dextromethorphan and GSK1034702

1.?In a clinical trial, a strong drug–drug interaction (DDI) was observed between dextromethorphan (DM, the object or victim drug) and GSK1034702 (the precipitant or perpetrator drug), following single and repeat doses. This study determined the inhibition parameters of GSK1034702 in vitro and applied PBPK modelling approaches to simulate the clinical observations and provide mechanistic hypotheses to understand the DDI.

2.?In vitro assays were conducted to determine the inhibition parameters of human CYP2D6 by GSK1034702. PBPK models were populated with the in vitro parameters and DDI simulations conducted and compared to the observed data from a clinical study with DM and GSK1034702.

3.?GSK1034702 was a potent direct and metabolism-dependent inhibitor of human CYP2D6, with inhibition parameters of: IC_50?=_?1.6?μM, K_inact?=?3.7?h^?1 and K_I?=?0.8?μM. Incorporating these data into PBPK models predicted a DDI after repeat, but not single, 5?mg doses of GSK1034702.

4.?The DDI observed with repeat administration of GSK1034702 (5?mg) can be attributed to metabolism-dependent inhibition of CYP2D6. Further, in vitro data were generated and several potential mechanisms proposed to explain the interaction observed following a single dose of GSK1034702.     

Clinical implications of dose-dependent cytochrome P-450 drug–drug interactions with antidepressants

The magnitude of drug–drug interactions in vitro involving competitive inhibition of cytochrome (CYP) isozymes by newer antidepressants can theoretically be shown to be dependent upon several factors. These include the concentration of both the substrate and inhibitor, the affinity of the inhibitor for the inhibited isozyme, and an inhibition constant. The purpose of this study was to compare the results from three human drug interaction studies with theoretical considerations and the results from in vitro studies. Of special interest was the relationship between dose or concentration of an enzyme inhibitor and the change in the plasma concentration of a co-administered drug. Observed data were fit to equations using linear regression and nonlinear least squares regression analysis. All three human data sets demonstrated a linear dose or concentration dependency in the magnitude of the observed drug–drug interaction. The results draw attention to the dose dependent nature of drug–drug interactions. As the dose of antidepressant is under clinician control, guidelines are suggested to minimize the clinical impact of antidepressant drug interactions. © 1998 John Wiley & Sons, Ltd.     

Aldehyde oxidase and its importance in novel drug discovery: present and future challenges

Introduction: Aldehyde oxidases (AOXs) are molybdo-flavoenzymes that oxidize aromatic aldehydes into the corresponding carboxylic acids and heterocycles into hydroxylated derivatives. AOXs have broad substrate specificity and are present in the liver of humans and many experimental animals. These enzymes play an important role in Phase I metabolism of drugs and xenobiotics of toxicological interest.

Areas covered: Preclinical studies on the AOX-dependent metabolism of new drug candidates are problematic. Furthermore, there is a general lack of reliable in silico methodologies to predict whether a new organic molecule is an AOX substrate. In vitro systems, for the design of high- or medium-throughput screening tests, to identify AOX substrates have many limitations. In vivo studies on AOX-dependent metabolism in animal models, on the other hand, are difficult because the complement of liver AOXs in humans and popular experimental animals is different. The authors discuss the possible ways to overcome all these problems.

Expert opinion: The significance of AOXs as drug-metabolizing enzymes is increasing, as the current strategies of organic synthesis designed to avoid cytochrome P450 (CYP450)-dependent metabolism tend to enrich for new chemical structures efficiently oxidized by these enzymes. There is need for reliable methods to screen for, predict, and validate AOX-dependent metabolism of new drug candidates.     

Mathematical modeling for novel cancer drug discovery and development

Introduction: Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments.

Areas covered: This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment.

Expert opinion: Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.     

Screening of drug metabolizing enzymes for fusidic acid and its interactions with isoform-selective substrates in vitro

1.?Fusidic acid (FA) is widely used for the treatment of infections of sensitive osteomyelitis or skin and soft tissue caused by bacteria. However, the role of cytochrome P450s (CYPs) in the metabolism of FA is unclear. In the present study, we screened the main CYPs for the metabolism of FA and studied its interactions with isoform-selective substrates in vitro.

2.?The main CYP450s were screened according to the inhibitory effect of specific inhibitors on the metabolism of FA in human liver microsomes (HLMs) or recombinant CYP isoforms. Enzyme kinetic parameters including K_i, K_i′, V_max, and IC₅₀ were calculated to determine the potential of FA to affect CYP-mediated metabolism of isoform-selective substrates.

3.?FA metabolism rate was inhibited by 49.8% and 83.1% under CYP2D6, CYP3A4 selective inhibitors in HLMs. In recombinant experiment, the inhibitory effects on FA metabolism were 83.3% for CYP2D6 and 58.9% for CYP3A4, respectively. FA showed inhibition on CYP2D6 and CYP3A4 with K_is of 13.9 and 38.6?μM, respectively. Other CYP isoforms including CYP1A2, CYP2A6, CYP2C9, CYP2E1, and CYP2C19 showed minimal or no effect on the metabolism of FA.

4.?FA was primarily metabolized by CYP2D6 and CYP3A4 and showed a noncompetitive inhibition on CYP2D6 and a mixed competitive inhibition on CYP3A4. Drug–drug interactions between FA and other chemicals, especially with substrates of CYP2D6 and CYP3A4, are phenomena that clinicians need to be aware of and cautious about.     

Cytochrome P450 2C9‐natural antiarthritic interactions: Evaluation of inhibition magnitude and prediction from in vitro data

Many dietary supplements are promoted to patients with osteoarthritis (OA) including the three naturally derived compounds, glucosamine, chondroitin and diacerein. Despite their wide spread use, research on interaction of these antiarthritic compounds with human hepatic cytochrome P450 (CYP) enzymes is limited. This study aimed to examine the modulatory effects of these compounds on CYP2C9, a major CYP isoform, using in vitro biochemical assay and in silico models. Utilizing valsartan hydroxylase assay as probe, all forms of glucosamine and chondroitin exhibited IC₅₀ values beyond 1000 μM, indicating very weak potential in inhibiting CYP2C9. In silico docking postulated no interaction with CYP2C9 for chondroitin and weak bonding for glucosamine. On the other hand, diacerein exhibited mixed‐type inhibition with IC₅₀ value of 32.23 μM and K_i value of 30.80 μM, indicating moderately weak inhibition. Diacerein's main metabolite, rhein, demonstrated the same mode of inhibition as diacerein but stronger potency, with IC₅₀ of 6.08 μM and K_i of 1.16 μM. The docking of both compounds acquired lower CDOCKER interaction energy values, with interactions dominated by hydrogen and hydrophobic bondings. The ranking with respect to inhibition potency for the investigated compounds was generally the same in both in vitro enzyme assay and in silico modeling with order of potency being diacerein/rhein > various glucosamine/chondroitin forms. In vitro‐in vivo extrapolation of inhibition kinetics (using 1 + [I]/K_i ratio) demonstrated negligible potential of diacerein to cause interaction in vivo, whereas rhein was predicted to cause in vivo interaction, suggesting potential interaction risk with the CYP2C9 drug substrates.     

In silico prediction of human serum albumin binding for drug leads

Introduction: Binding of drugs to human serum albumin (HSA) strongly influences their pharmacokinetic behavior and is associated with drug safety issues, low clearance, low brain penetration, as well as drug-drug interactions. Thus, in silico prediction of HSA binding contributes significantly to the discovery of new drug candidates.

Areas covered: The authors provide a short overview on the principles of HSA binding and the crystal structure of HSA, as well as discussing and analyzing the recent structure- and ligand-based HSA binding models. The authors also present the advantages and limitations of each methodology to construct efficient local or global models and outline the critical structural features contributing to HSA.

Expert opinion: The in silico estimation of drug binding to HSA in early drug discovery contributes to the lead optimization process. Local models are useful for the design of new compounds with reduced HSA binding for a particular target receptor, while real-time quantitative structure-activity relationships or global models combining structure- and ligand-based approaches serve for compound libraries screening. However, research efforts on other important plasma proteins should be strengthened in the perspective to enable predictions of total plasma protein binding for clinical candidates.     

Challenges with the precise prediction of ABC-transporter interactions for improved drug discovery

Introduction: Given that membrane efflux transporters can influence a drug’s pharmacokinetics, efficacy and safety, identifying potential substrates and inhibitors of these transporters is a critical element in the drug discovery and development process. Additionally, it is important to predict the inhibition potential of new drugs to avoid clinically significant drug interactions. The goal of preclinical studies is to characterize a new drug as a substrate or inhibitor of efflux transporters.

Areas covered: This article reviews preclinical systems that are routinely utilized to determine whether a new drug is substrate or inhibitor of efflux transporters including in silico models, in vitro membrane and cell assays, and animal models. Also included is an examination of studies comparing in vitro inhibition data to clinical drug interaction outcomes.

Expert opinion: While a number of models are employed to classify a drug as an efflux substrate or inhibitor, there are challenges in predicting clinical drug interactions. Improvements could be made in these predictions through a tier approach to classify new drugs, validation of preclinical assays, and refinement of threshold criteria for clinical interaction studies.     

Nationwide drug-dispensing data reveal important differences in adherence to drug label recommendations on CYP2D6-dependent drug interactions

AIMS

The study aimed to investigate the clinical adherence to drug label recommendations on important drug–drug interactions (DDIs). Dispensing data on drug combinations involving selective serotonin reuptake inhibitor (SSRI) antidepressants could help to identify areas for intensified medical education.

METHODS

This was a retrospective, cross-sectional analysis of individual dispensing data regarding all individuals ≥15 years old in Sweden. The study analysed the prescribing and dispensing of CYP2D6 drugs (metoprolol, donepezil, galantamine, codeine, tamoxifen) together with CYP2D6-blocking SSRIs (paroxetine/fluoxetine) or SSRIs without significant CYP2D6 inhibition (citalopram/escitalopram/sertraline), and the related prescribing of CYP2D6-independent comparator drugs (atenolol, rivastigmine, propoxyphene, anastrozole). Odds were calculated between each CYP2D6 drug and the corresponding comparator drug in patients on fluoxetine/paroxetine and citalopram/escitalopram/sertraline, respectively. The odds ratio (OR) was calculated by dividing the obtained odds in patients on fluoxetine/paroxetine by the corresponding odds in patients on citalopram/escitalopram/sertraline.

RESULTS

Compared with patients that were dispensed citalopram/escitalopram/sertraline, patients dispensed fluoxetine/paroxetine had lower prescribing rates of metoprolol (adjusted OR 0.80; 95% confidence interval 0.76, 0.85), donepezil (0.65; 0.49, 0.86) and galantamine (0.58; 0.41, 0.81). In contrast, the use of prodrugs codeine (compared woth propoxyphene) or tamoxifen (compared with anastrozole) was similar among patients on fluoxetine/paroxetine and citalopram/escitalopram/sertraline (adjusted OR 1.03; 0.94, 1.12 and 1.29; 0.96, 1.73, respectively).

CONCLUSIONS

Clinically important DDIs that are associated with impaired bioactivation of prodrugs might be more easily neglected in clinical practice compared with DDIs that cause drug accumulation and symptomatic adverse drug reactions.     

Evaluation of the effects of 18 non-synonymous single-nucleotide polymorphisms of CYP450 2C19 on in vitro drug inhibition potential by a fluorescence-based high-throughput assay

1. To comprehensively understand the effects of CYP2C19 genetic polymorphisms on inhibition-based drug–drug interactions (DDIs), 18 human CYP2C19 non-synonymous single-nucleotide polymorphic variants and the wild-type isoform (CYP2C19.1A) were expressed in yeast cells. Using a fluorescence-based high-throughput method, the kinetic constants of these variants, as well as the inhibition constants for 10 drugs, were determined.

2. CYP2C19.5B and CYP2C19.6 showed no activity towards CEC (3-cyano-7-ethoxycoumarin) O-deethylation. CYP2C19.8, CYP2C19.9, CYP2C19.10, CYP2C19.16, CYP2C19.19, E122A and A161P* (an allele containing both A161P and I331V) exhibited significantly reduced catalytic activities compared with CYP2C19.1A. The inhibition assay showed that the CYP2C19 genotype significantly affected the in vitro drug inhibition potential. Although the effect on drug inhibition potential is genotype- and inhibitor-dependent, there was an obvious trend: drugs tended to exhibit higher IC₅₀ values (i.e. decreased inhibition potential) towards variants with reduced activity compared with variants with normal activity. This indicated that patients with reduced-function alleles may be less susceptible to CYP2C19-related DDIs.

3. In this study, we provided the first in vitro evidence of CYP2C19 genotype-dependent effects on drug inhibition potential. This work greatly extends our understanding of the functional consequences of CYP2C19 genetic polymorphisms, and thus should prove valuable for CYP2C19 genotype-based therapy.