Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery$

Abstract

Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

Prediction of intestinal absorption and blood-brain barrier penetration by computational methods

This review surveys the computational methods that have been developed with the aim of identifying drug candidates likely to fail later on the road to market. The specifications for such computational methods are outlined, including factors such as speed, interpretability, robustness and accuracy. Then, computational filters aimed at predicting "drug-likeness" in a general sense are discussed before methods for the prediction of more specific properties--intestinal absorption and blood-brain barrier penetration--are reviewed. Directions for future research are discussed and, in concluding, the impact of these methods on the drug discovery process, both now and in the future, is briefly considered.     

Applications of high throughput microsomal stability assay in drug discovery

High throughput in vitro microsomal stability assays are widely used in drug discovery as an indicator for in vivo stability, which affects pharmacokinetics. This is based on in-depth research involving a limited number of model drug-like compounds that are cleared predominantly by cytochrome P450 metabolism. However, drug discovery compounds are often not drug-like, are assessed with high throughput assays, and have many potential uncharacterized in vivo clearance mechanisms. Therefore, it is important to determine the correlation between high throughput in vitro microsomal stability data and abbreviated discovery in vivo pharmacokinetics study data for a set of drug discovery compounds in order to have evidence for how the in vitro assay can be reliably applied by discovery teams for making critical decisions. In this study the relationship between in vitro single time point high throughput microsomal stability and in vivo clearance from abbreviated drug discovery pharmacokinetics studies was examined using 306 real world drug discovery compounds. The results showed that in vitro Phase I microsomal stability t(1/2) is significantly correlated to in vivo clearance with a p-value<0.001. For compounds with low in vitro rat microsomal stability (t(1/2)<15 min), 87% showed high clearance in vivo (CL>25 mL/min/kg). This demonstrates that high throughput microsomal stability data are very effective in identifying compounds with significant clearance liabilities in vivo. For compounds with high in vitro rat microsomal stability (t(1/2)>15 min), no significant differentiation was observed between high and low clearance compounds. This is likely owing to other clearance pathways, in addition to cytochrome P450 metabolism that enhances in vivo clearance. This finding supports the strategy used by medicinal chemists and drug discovery teams of applying the in vitro data to triage compounds for in vivo PK and efficacy studies and guide structural modification to improve metabolic stability. When in vitro and in vivo data are both available for a compound, potential in vivo clearance pathways can be diagnosed to guide further discovery studies.     

“类药性”：预测与实践

类药性是人们在对安全、可以口服的药物的性质的研究中产生的概念。在新药研发过程中,化合物类药性与活性居于重要的地位。类药性特征包括三方面：（1）物理化学性质特征;（2）拓扑结构特征;（3）药代动力学（吸收,分布,代谢,排泄）性质。本文综述了类药性研究的最新进展,并对今后的研究内容进行了展望。     

Pharmacophore features of potential drugs

Drug discovery efforts rely increasingly on the identification of quality lead compounds through high-throughput synthesis and screening. However, large-scale random libraries have yielded only a low number of quality lead molecules. To address this shortcoming researchers have paid more attention to the concept of "drug-likeness" of molecules in combinatorial and screening libraries. Database profiling and analysis methods have been employed to identify the structural features of known drug molecules. Neural networks and machine learning methods help to distinguish between drugs and nondrugs. More recently, database-independent pharmacophore filters have been introduced that provide simple intuitive rules to classify potential drugs.     

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

A new quantification method of drug-likeness based on unsupervised learning. The method only uses drug molecules as training set without any non-drug-like molecules.     

Application of self-organizing maps in compounds pattern recognition and combinatorial library design

In the computer-aided drug design, in order to find some new leads from a large library of compounds, the pattern recognition study of the diversity and similarity assessment of the chemical compounds is required; meanwhile in the combinatorial library design, more attention is given to design target focusing library along with diversity and drug-likeness criteria. This review presents the current state-of-art applications of Kohonen self-organizing maps (SOM) for studying the compounds pattern recognition, comparing the property of molecular surfaces, distinguishing drug-like and nondrug-like molecules, splitting a dataset into the proper training and test sets before constructing a QSAR (Quantitative Structural-Activity Relationship) model, and also for the combinatorial libraries comparison and the combinatorial library design. The Kohonen self-organizing map will continue to play an important role in drug discovery and library design.     

Current Status,Distribution, and Future Directions of Natural Products against Colorectal Cancer in Indonesia: A Systematic Review

In 2020, an estimated 19.3 million new cancer cases and nearly 10 million cancer deaths have occurred worldwide, with colorectal cancer ranking as the third most frequently diagnosed (10.0%). Several attempts have been conducted against cancer, including surgery, radiation, monoclonal antibodies, and chemotherapy. Many people choose natural products as alternatives against cancer. These products will not only help in human life preservation but also work as a source of up-to-date information, leading people away from incorrect information. We discuss the current status, distribution, and future implications of protecting populations with natural products as an alternative against colorectal cancer in Indonesia. Thirty-eight studies were included in this review for data extraction. The distribution of natural products in Indonesia that have potential activity against colorectal cancer cells was predominated by terpenoids, followed by phytosterols, phenolics, alkaloids, and polyisoprenoids. The type of cell line utilized in the cytotoxic activity analysis of natural products was the WiDr cell line, followed by HT-29 cells and HCT-116 cells. This review showed that MTT in vitro assay is a general method used to analyze the cytotoxic activity of a natural product against colorectal cancer cells, followed by other in vitro and in vivo methods. The systematic review provided predictions for several secondary metabolites to be utilized as an alternative treatment against colorectal cancer in Indonesia. It also might be a candidate for a future co-chemotherapy agent in safety, quality, and standardization. In addition, computational methods are being developed to predict the drug-likeness of compounds, thus, drug discovery is already on the road towards electronic research and development.     

Discovery of novel targets with high throughput RNA interference screening

High throughput technologies have the potential to affect all aspects of drug discovery. Considerable attention is paid to high throughput screening (HTS) for small molecule lead compounds. The identification of the targets that enter those HTS campaigns had been driven by basic research until the advent of genomics level data acquisition such as sequencing and gene expression microarrays. Large-scale profiling approaches (e.g., microarrays, protein analysis by mass spectrometry, and metabolite profiling) can yield vast quantities of data and important information. However, these approaches usually require painstaking in silico analysis and low-throughput basic wet-lab research to identify the function of a gene and validate the gene product as a potential therapeutic drug target. Functional genomic screening offers the promise of direct identification of genes involved in phenotypes of interest. In this review, RNA interference (RNAi) mediated loss-of-function screens will be discussed and as well as their utility in target identification. Some of the genes identified in these screens should produce similar phenotypes if their gene products are antagonized with drugs. With a carefully chosen phenotype, an understanding of the biology of RNAi and appreciation of the limitations of RNAi screening, there is great potential for the discovery of new drug targets.     

Applicability of bioanalysis of multiple analytes in drug discovery and development: review of select case studies including assay development considerations

The development of sound bioanalytical method(s) is of paramount importance during the process of drug discovery and development culminating in a marketing approval. Although the bioanalytical procedure(s) originally developed during the discovery stage may not necessarily be fit to support the drug development scenario, they may be suitably modified and validated, as deemed necessary. Several reviews have appeared over the years describing analytical approaches including various techniques, detection systems, automation tools that are available for an effective separation, enhanced selectivity and sensitivity for quantitation of many analytes. The intention of this review is to cover various key areas where analytical method development becomes necessary during different stages of drug discovery research and development process. The key areas covered in this article with relevant case studies include: (a) simultaneous assay for parent compound and metabolites that are purported to display pharmacological activity; (b) bioanalytical procedures for determination of multiple drugs in combating a disease; (c) analytical measurement of chirality aspects in the pharmacokinetics, metabolism and biotransformation investigations; (d) drug monitoring for therapeutic benefits and/or occupational hazard; (e) analysis of drugs from complex and/or less frequently used matrices; (f) analytical determination during in vitro experiments (metabolism and permeability related) and in situ intestinal perfusion experiments; (g) determination of a major metabolite as a surrogate for the parent molecule; (h) analytical approaches for universal determination of CYP450 probe substrates and metabolites; (i) analytical applicability to prodrug evaluations-simultaneous determination of prodrug, parent and metabolites; (j) quantitative determination of parent compound and/or phase II metabolite(s) via direct or indirect approaches; (k) applicability in analysis of multiple compounds in select disease areas and/or in clinically important drug-drug interaction studies. A tabular representation of select examples of analysis is provided covering areas of separation conditions, validation aspects and applicable conclusion. A limited discussion is provided on relevant aspects of the need for developing bioanalytical procedures for speedy drug discovery and development. Additionally, some key elements such as internal standard selection, likely issues of mass detection, matrix effect, chiral aspects etc. are provided for consideration during method development.     

Automatic tailoring and transplanting: a practical method that makes virtual screening more useful

Docking-based virtual screening of large compound libraries has been widely applied to lead discovery in structure-based drug design. However, subsequent lead optimizations often rely on other types of computational methods, such as de novo design methods. We have developed an automatic method, namely automatic tailoring and transplanting (AutoT&T), which can effectively utilize the outcomes of virtual screening in lead optimization. This method detects suitable fragments on virtual screening hits and then transplants them onto a lead compound to generate new ligand molecules. Binding affinities, synthetic feasibilities, and drug-likeness properties are considered in the selection of final designs. In this study, our AutoT&T program was tested on three different target proteins, including p38 MAP kinase, PPAR-α, and Mcl-1. In the first two cases, AutoT&T was able to produce molecules identical or similar to known inhibitors with better potency than the given lead compound. In the third case, we demonstrated how to apply AutoT&T to design novel ligand molecules from scratch. Compared to the solutions generated by other two de novo design methods, i.e., LUDI and EA-Inventor, the solutions generated by AutoT&T were structurally more diverse and more promising in terms of binding scores in all three cases. AutoT&T also completed the assigned jobs more efficiently than LUDI and EA-Inventor by several folds. Our AutoT&T method has certain technical advantages over de novo design methods. Importantly, it expands the application of virtual screening from lead discovery to lead optimization and thus may serve as a valuable tool for many researchers.     

Incurred sample reanalysis in drug discovery bioanalysis

Bioanalysis plays a key role during the drug discovery process to generate the pharmacokinetic data to facilitate unbiased evaluation of leads, optimized leads and drug candidates. Such pharmacokinetic data are used to enable key decisions in the drug discovery process. The aim of the work is to put forward a new strategy of performing the incurred sample reanalysis for select small molecule novel chemical entities at different stages of drug discovery prior to candidate selection. Three discovery programs representing hits, leads and optimized lead candidates were selected for the incurred sample reanalysis (ISR) analysis. From each discovery program, two novel chemical entities were selected for the ISR analysis. The time points considered for ISR generally varied among the programs; however, samples coinciding with drug absorption, distribution and elimination were considered in the ISR assessment. With the exception of a single ISR value that gave a high deviation (about 63%), the observed ISR values supported the discovery bioanalytical assays. While the individual bioanalytical laboratory can draw an algorithm for selecting novel chemical entities and fixing the acceptance criteria for the ISR data, it is proposed that the percentage difference between ISR vs. original concentration for 67% of the repeat samples is contained within ±30% for discovery bioanalysis.     

Ion-pair extraction of some sympathomimetics: Description of an extraction model for terbutaline and investigation of some factors influencing the recovery of sympathomimetics

The ion-pair formation of terbutaline, a resorcinolamine, was studied during extraction into ethyl acetate, a slightly polar organic solvent. Factors influencing the extraction, e.g., the concentration of various ions in the aqueous phase, are considered. Terbutaline is extracted as a simple ion-pair without the formation of higher adducts. The extraction of some Sympathomimetics is also described with respect to the nature of the organic solvent, the drug and the extracting anion. Factor analysis is applied to the data, and the reliability of predicted values is discussed. A search for a correlation between the factor analysis parameters characterizing the aqueous—organic phase systems and their physical properties led to a correlation between these parameters and interfacial tension; this result is explained by the macroscopic model of a solid surface (i.e., of the extracted compound) in contact with two fluids.     

Computational efforts to identify natural occurring compounds from phyllanthus niruri that target hepatitis B viral infections: DFT,docking and dynamics simulation study

Hepatitis B virus (HBV) is an enveloped hepatotropic virus responsible for nucleic acids replication. It causes chronic infection. Depending on the strain, mutations in the core protein of chronic hepatitis B virus (HBV) infections occur. Medicinal plants, the backbone of traditional medicine, are a potential source of lead molecules in drug discovery due to extensive pharmacological studies. In this study, we have screened twenty-nine phytochemicals. The ADME and drug-likeness of these phytochemicals were investigated. After screening, the binding affinity of ten phytochemicals was studied through molecular docking. Simulation studies were carried out for 100 ns to analyze the properties of RMSD, Rg, RMSF, average hydrogen bond number and SASA of hepatitis B virus capsid protein. As per the docking results phyllanthosterol, may be used as a potential inhibitors against HBV. The simulations findings revealed that, in case of mutant protein, the flexibility nature decreases as compared to wildtype protein. Our results may provide useful information for drug design and to lead the identification of novel inhibitor for hepatotropic viral infection.     

Molecular docking,ADMET analysis,and bioactivity studies of phytochemicals from Phyllanthus niruri as potential inhibitors of hepatitis C virus NSB5 polymerase

Out of the liver complications, hepatitis C has been reported to be treated with antiviral medications which are quite expensive and have severe side effects on health. Therefore, the main target of this work is to search for a safer and effective remedy for hepatitis C from the reservoir of phytochemicals present in Phyllanthus niruri via in-silico studies. Reported phytochemicals isolated from Phyllanthus niruri were subjected to molecular docking simulation using PyRx docking tool, PyMol, and Biovia 2019 for visualization against Hepatitis C virus (HCV) NSB5 polymerase. However, the docking scores with all the other necessary analyses like drug-likeness, and ADMET profiling, furnished only three of the screened ligands as very potent potential drug candidates as compared to the standard drug of HCV, mericitabine(-8.1 ?kcal/mol). Therefore, cyanidine (?8.7 ?kcal/mol), lupeol(-8.5 ?kcal/mol), phloretin-2-O-beta glucoside (?8.3 ?kcal/mol) with excellent drug-likeness, and ADMET properties are hereby recommended for further in vivo animal studies and clinical trials towards the development of new therapeutic agent for Hepatitis C Virus treatment and management.     

False-Positive Rate Determination of Protein Target Discovery using a Covalent Modification- and Mass Spectrometry-Based Proteomics Platform

Detection and quantitation of protein–ligand binding interactions is important in many areas of biological research. Stability of proteins from rates of oxidation (SPROX) is an energetics-based technique for identifying the proteins targets of ligands in complex biological mixtures. Knowing the false-positive rate of protein target discovery in proteome-wide SPROX experiments is important for the correct interpretation of results. Reported here are the results of a control SPROX experiment in which chemical denaturation data is obtained on the proteins in two samples that originated from the same yeast lysate, as would be done in a typical SPROX experiment except that one sample would be spiked with the test ligand. False-positive rates of 1.2-2.2 % and <0.8 % are calculated for SPROX experiments using Q-TOF and Orbitrap mass spectrometer systems, respectively. Our results indicate that the false-positive rate is largely determined by random errors associated with the mass spectral analysis of the isobaric mass tag (e.g., iTRAQ®) reporter ions used for peptide quantitation. Our results also suggest that technical replicates can be used to effectively eliminate such false positives that result from this random error, as is demonstrated in a SPROX experiment to identify yeast protein targets of the drug, manassantin A. The impact of ion purity in the tandem mass spectral analyses and of background oxidation on the false-positive rate of protein target discovery using SPROX is also discussed.