In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a ‘parallel progression approach to drug design’ applied to β-blockers

The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein ‘parallel’ information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the ‘parallel progression approach’. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log V_d) and clearance (log Cl), which together influence the half-life (t_1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pK_i have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pK_i values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the ‘parallel progression approach’ helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.     

Integrating computer-based de novo drug design and multidimensional filtering for desirable drugs

In the pharmaceutical industry today, many of the potent compounds discovered using expensive technologies are eventually rejected because of poor physicochemical or absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) properties. This problem can be addressed by placing fast and accurate computational technologies at the heart of drug discovery. Chemically diverse and potent compounds generated by de novo design algorithms are scored for ADME/Tox properties using rigorously validated statistical models. Every molecule passing through this in silico pipeline is thus associated with a wealth of predicted properties, thereby allowing for rapid assessment to determine which molecule should be further developed. Critical to this idea is a platform that allows for the efficient exchange of in silico and experimental data between all scientists regardless of specialization. By bridging the gap between the in silico and experimental cultures in this fashion, an information-driven, cost-effective drug discovery program can be realized.     

Pharmacokinetic de-risking tools for selection of monoclonal antibody lead candidates

Pharmacokinetic studies play an important role in all stages of drug discovery and development. Recent advancements in the tools for discovery and optimization of therapeutic proteins have created an abundance of candidates that may fulfill target product profile criteria. Implementing a set of in silico, small scale in vitro and in vivo tools can help to identify a clinical lead molecule with promising properties at the early stages of drug discovery, thus reducing the labor and cost in advancing multiple candidates toward clinical development. In this review, we describe tools that should be considered during drug discovery, and discuss approaches that could be included in the pharmacokinetic screening part of the lead candidate generation process to de-risk unexpected pharmacokinetic behaviors of Fc-based therapeutic proteins, with an emphasis on monoclonal antibodies.     

Recent applications of protein crystallography and structure-guided drug design

Technological advances to increase the throughput of purified protein production and co-crystallization of target proteins with small molecules have helped to solidify the role that structure via crystallography has on drug discovery. Visualization of how drug-like molecules bind to the target protein is a key step in driving follow-up or preclinical chemistry to improve characteristics of the molecule. Using structural information to guide small-molecule design and generate new chemical ideas is now a mainstay in the drug discovery process.     

FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects

Background  

Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performedin silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.     

Mobile genetic elements: in silico,in vitro,in vivo

Mobile genetic elements (MGEs), also called transposable elements (TEs), represent universal components of most genomes and are intimately involved in nearly all aspects of genome organization, function and evolution. However, there is currently a gap between the fast pace of TE discovery in silico, driven by the exponential growth of comparative genomic studies, and a limited number of experimental models amenable to more traditional in vitro and in vivo studies of structural, mechanistic and regulatory properties of diverse MGEs. Experimental and computational scientists came together to bridge this gap at a recent conference, ‘Mobile Genetic Elements: in silico, in vitro, in vivo’, held at the Marine Biological Laboratory (MBL) in Woods Hole, MA, USA.     

Structure based virtual screening, 3D-QSAR,molecular dynamics and ADMET studies for selection of natural inhibitors against structural and non-structural targets of Chikungunya

The transmission of mosquito-borne Chikungunya virus (CHIKV) has large epidemics worldwide. Till date, there are neither anti-viral drugs nor vaccines available for the treatment of Chikungunya. Accumulated evidences suggest that some natural compounds i.e., Epigallocatechin gallate, Harringtonine, Apigenin, Chrysin, Silybin, etc. have the capability to inhibit CHIKV replication in vitro. Natural compounds are known to possess less or no side effects. Therefore, natural compound in its purified or crude extracts form could be the preeminent and safe mode of therapies for Chikungunya. Wet lab screening and identification of natural compounds against Chikungunya targets is a time consuming and expensive exercise. In the present study, we used in silico techniques like receptor-ligand docking, Molecular dynamic (MD), Three Dimensional Quantitative Structure Activity Relation (3D-QSAR) and ADME properties to screen out potential compounds. Aim of the study is to identify potential lead/s from natural sources using in silico techniques that can be developed as a drug like molecule against Chikungunya infection and replication. Three softwares were used for molecular docking studies. Potential ligands selected by docking studies were subsequently subjected 3D-QSAR studies to predict biological activity. Based on docking scores and pIC₅₀ value, potential anti-Chikungunya compounds were identified. Best docked receptor-ligands were also subjected to MD for more accurate estimation. Lipinski’s rule and ADME studies of the identified compounds were also studied to assess their drug likeness properties. Results of in silico findings, led to identification of few best fit compounds of natural origin against targets of Chikungunya virus which may lead to discovery of new drugs for Chikungunya.

Communicated by Ramaswamy H. Sarma     

Improving Compound Quality through in vitro and in silico Physicochemical Profiling

Many compounds entering clinical studies do not survive the numerous hurdles for a good pharmacological lead to a drug on the market. The reasons for attrition have been widely studied which resulted in more early attention to compound quality related to physical chemistry, drug metabolism and pharmacokinetics (DMPK), and toxicology/safety. This paper will briefly review current physicochemical in vitro assays and in silico predictions to support compound and library design through to lead optimization. The most important physicochemical properties include lipophilicity (log P/D), pK_a, solubility, and permeability. These drive key ADMET properties such as absorption, cell penetration, access to the brain, volume of distribution, plasma protein binding, metabolism, and toxicity, as well as biopharmaceutical behavior. Much data are now available from medium‐ to high‐throughput physchem and ADMET in vitro assays, either in the public domain (see, e.g., PubChem, PubMed) or in drug companies' in‐house databases. Such data are increasingly being computer‐modelled and used in predictive chemistry. New pipelining technology makes it easier to build and update QSAR models so that such models can use the latest available data to produce robust local and global predictive in silico ADMET models.     

Selection of bead‐displayed,PNA‐encoded chemicals

The lack of efficient identification and isolation methods for specific molecular binders has fundamentally limited drug discovery. Here, we have developed a method to select peptide nucleic acid (PNA) encoded molecules with specific functional properties from combinatorially generated libraries. This method consists of three essential stages: (1) creation of a Lab‐on‐Bead? library, a one‐bead, one‐sequence library that, in turn, displays a library of candidate molecules, (2) fluorescence microscopy‐aided identification of single target‐bound beads and the extraction – wet or dry – of these beads and their attached candidate molecules by a micropipette manipulator, and (3) identification of the target‐binding candidate molecules via amplification and sequencing. This novel integration of techniques harnesses the sensitivity of DNA detection methods and the multiplexed and miniaturized nature of molecule screening to efficiently select and identify target‐binding molecules from large nucleic acid encoded chemical libraries. Beyond its potential to accelerate assays currently used for the discovery of new drug candidates, its simple bead‐based design allows for easy screening over a variety of prepared surfaces that can extend this technique's application to the discovery of diagnostic reagents and disease markers. Copyright © 2009 John Wiley & Sons, Ltd.     

Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a transformative tool in biotechnology.     

Prioritizing covariates in the planning of future studies in the meta‐analytic framework

Science can be seen as a sequential process where each new study augments evidence to the existing knowledge. To have the best prospects to make an impact in this process, a new study should be designed optimally taking into account the previous studies and other prior information. We propose a formal approach for the covariate prioritization, that is the decision about the covariates to be measured in a new study. The decision criteria can be based on conditional power, change of the p‐value, change in lower confidence limit, Kullback–Leibler divergence, Bayes factors, Bayesian false discovery rate or difference between prior and posterior expectation. The criteria can be also used for decisions on the sample size. As an illustration, we consider covariate prioritization based on genome‐wide association studies for C‐reactive protein levels and make suggestions on the genes to be studied further.     

Molecular modeling study for the design of novel acetyl-CoA carboxylase inhibitors using 3D QSAR,molecular docking and dynamic simulations

Acetyl-CoA carboxylase (ACC) enzyme plays an important role in the regulation of biosynthesis and oxidation of fatty acids. ACC is a recognized drug target for the treatment of obesity and diabetes. Combination of ligand and structure-based in silico methods along with activity and toxicity prediction provides best lead compounds in the drug discovery process. In this study, a data-set of 100 ACC inhibitors were used for the development of comparative molecular field analysis (CoMFA) and comparative molecular similarity index matrix analysis (CoMSIA) models. The generated contour maps were used for the design of novel ACC inhibitors. CoMFA and CoMSIA models were used for the predication of activity of designed compounds. In silico toxicity risk prediction study was carried out for the designed compounds. Molecular docking and dynamic simulations studies were performed to know the binding mode of designed compounds with the ACC enzyme. The designed compounds showed interactions with key amino acid residues important for catalysis, and good correlation was observed between binding free energy and inhibition of ACC.     

Zika virus NS5 protein potential inhibitors: an enhanced in silico approach in drug discovery

The re-emerging Zika virus (ZIKV) is an arthropod-borne virus that has been described to have explosive potential as a worldwide pandemic. The initial transmission of the virus was through a mosquito vector, however, evolving modes of transmission has allowed the spread of the disease over continents. The virus has already been linked to irreversible chronic central nervous system conditions. The concerns of the scientific and clinical community are the consequences of Zika viral mutations, thus suggesting the urgent need for viral inhibitors. There have been large strides in vaccine development against the virus but there are still no FDA approved drugs available. Rapid rational drug design and discovery research is fundamental in the production of potent inhibitors against the virus that will not just mask the virus, but destroy it completely. In silico drug design allows for this prompt screening of potential leads, thus decreasing the consumption of precious time and resources. This study demonstrates an optimized and proven screening technique in the discovery of two potential small molecule inhibitors of ZIKV Methyltransferase and RNA dependent RNA polymerase. This in silico ‘per-residue energy decomposition pharmacophore’ virtual screening approach will be critical in aiding scientists in the discovery of not only effective inhibitors of Zika viral targets, but also a wide range of anti-viral agents.     

Commentary: Why Pharmaceutical Scientists in Early Drug Discovery Are Critical for Influencing the Design and Selection of Optimal Drug Candidates

This commentary reflects the collective view of pharmaceutical scientists from four different organizations with extensive experience in the field of drug discovery support. Herein, engaging discussion is presented on the current and future approaches for the selection of the most optimal and developable drug candidates. Over the past two decades, developability assessment programs have been implemented with the intention of improving physicochemical and metabolic properties. However, the complexity of both new drug targets and non-traditional drug candidates provides continuing challenges for developing formulations for optimal drug delivery. The need for more enabled technologies to deliver drug candidates has necessitated an even more active role for pharmaceutical scientists to influence many key molecular parameters during compound optimization and selection. This enhanced role begins at the early in vitro screening stages, where key learnings regarding the interplay of molecular structure and pharmaceutical property relationships can be derived. Performance of the drug candidates in formulations intended to support key in vivo studies provides important information on chemotype-formulation compatibility relationships. Structure modifications to support the selection of the solid form are also important to consider, and predictive in silico models are being rapidly developed in this area. Ultimately, the role of pharmaceutical scientists in drug discovery now extends beyond rapid solubility screening, early form assessment, and data delivery. This multidisciplinary role has evolved to include the practice of proactively taking part in the molecular design to better align solid form and formulation requirements to enhance developability potential.     

Access Path to the Ligand Binding Pocket May Play a Role in Xenobiotics Selection by AhR

Understanding of multidrug binding at the atomic level would facilitate drug design and strategies to modulate drug metabolism, including drug transport, oxidation, and conjugation. Therefore we explored the mechanism of promiscuous binding of small molecules by studying the ligand binding domain, the PAS-B domain of the aryl hydrocarbon receptor (AhR). Because of the low sequence identities of PAS domains to be used for homology modeling, structural features of the widely employed HIF-2α and a more recent suitable template, CLOCK were compared. These structures were used to build AhR PAS-B homology models. We performed molecular dynamics simulations to characterize dynamic properties of the PAS-B domain and the generated conformational ensembles were employed in in silico docking. In order to understand structural and ligand binding features we compared the stability and dynamics of the promiscuous AhR PAS-B to other PAS domains exhibiting specific interactions or no ligand binding function. Our exhaustive in silico binding studies, in which we dock a wide spectrum of ligand molecules to the conformational ensembles, suggest that ligand specificity and selection may be determined not only by the PAS-B domain itself, but also by other parts of AhR and its protein interacting partners. We propose that ligand binding pocket and access channels leading to the pocket play equally important roles in discrimination of endogenous molecules and xenobiotics.     

Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations

Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer‐aided drug discovery, a systematic tool encompassing multiple steps from druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top‐ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure‐based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker , can be used in conjunction with other tools available in ProDy.     

Intracellular antibodies and cancer: New technologies offer therapeutic opportunities

Since the realisation that the antigen‐binding regions of antibodies, the variable (V) regions, can be uncoupled from the rest of the molecule to create fragments that recognise and abrogate particular protein functions in cells, the use of antibody fragments inside cells has become an important tool in bioscience. Diverse libraries of antibody fragments plus in vivo screening can be used to isolate single chain variable fragments comprising VH and VL segments or single V‐region domains. Some of these are interfering antibody fragments that compete with protein‐protein interactions, providing lead molecules for drug interactions that until now have been considered difficult or undruggable. It may be possible to deliver or express antibody fragments in target cells as macrodrugs per se. In future incarnations of intracellular antibodies, however, the structural information of the interaction interface of target and antibody fragment should facilitate development of binding site mimics as small drug‐like molecules. This is a new dawn for intracellular antibody fragments both as macrodrugs and as precursors of drugs to treat human diseases and should finally lead to the removal of the epithet of the ‘undruggable’ protein‐protein interactions.     

Increasingly accurate dynamic molecular models of G-protein coupled receptor oligomers: Panacea or Pandora's box for novel drug discovery?

For years, conventional drug design at G-protein coupled receptors (GPCRs) has mainly focused on the inhibition of a single receptor at a usually well-defined ligand-binding site. The recent discovery of more and more physiologically relevant GPCR dimers/oligomers suggests that selectively targeting these complexes or designing small molecules that inhibit receptor–receptor interactions might provide new opportunities for novel drug discovery. To uncover the fundamental mechanisms and dynamics governing GPCR dimerization/oligomerization, it is crucial to understand the dynamic process of receptor–receptor association, and to identify regions that are suitable for selective drug binding. This minireview highlights current progress in the development of increasingly accurate dynamic molecular models of GPCR oligomers based on structural, biochemical, and biophysical information that has recently appeared in the literature. In view of this new information, there has never been a more exciting time for computational research into GPCRs than at present. Information-driven modern molecular models of GPCR complexes are expected to efficiently guide the rational design of GPCR oligomer-specific drugs, possibly allowing researchers to reach for the high-hanging fruits in GPCR drug discovery, i.e. more potent and selective drugs for efficient therapeutic interventions.     

Predicting Ligand Interactions with ABC Transporters in ADME

ABC‐type drug efflux pumps, e.g., ABCB1 (=P‐glycoprotein, =MDR1), ABCC1 (=MRP1), and ABCG2 (=MXR, =BCRP), confer a multi‐drug resistance (MDR) phenotype to cancer cells. Furthermore, the important contribution of ABC transporters for bioavailability, distribution, elimination, and blood–brain barrier permeation of drug candidates is increasingly recognized. This review presents an overview on the different computational methods and models pursued to predict ABC transporter substrate properties of drug‐like compounds. They encompass ligand‐based approaches ranging from ‘simple rule’‐based efforts to sophisticated machine learning methods. Many of these models show excellent performance for the data sets used. However, due to the complex nature of the applied methods, useful interpretation of the models that can be directly translated into chemical structures by the medicinal chemist is rather difficult. Additionally, very recent and promising attempts in the field of structure‐based modeling of ABC transporters, which embody homology modeling as well as recently published X‐ray structures of murine ABCB1, will be discussed.