Expert Opinion on Drug Discovery ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/iedc20 Open access in silico tools to predict the ADMET profiling of drug candidates Supratik Kar & Jerzy Leszczynski To cite this article: Supratik Kar & Jerzy Leszczynski (2020): Open access in silico tools to predict the ADMET profiling of drug candidates, Expert Opinion on Drug Discovery, DOI: 10.1080/17460441.2020.1798926 To link to this article: https://doi.org/10.1080/17460441.2020.1798926 Published online: 31 Jul 2020. Submit your article to this journal View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=iedc20 EXPERT OPINION ON DRUG DISCOVERY https://doi.org/10.1080/17460441.2020.1798926 REVIEW Open access in silico tools to predict the ADMET profiling of drug candidates Supratik Kar and Jerzy Leszczynski Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, USA ABSTRACT ARTICLE HISTORY Introduction: We are in an era of bioinformatics and cheminformatics where we can predict data in the Received 30 March 2020 fields of medicine, the environment, engineering and public health. Approaches with open access in Accepted 17 July 2020 silico tools have revolutionized disease management due to early prediction of the absorption, dis­ KEYWORDS tribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco- ADMET; drug; in silico; open friendly next-generation drugs. access; prediction Areas covered: This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design. Expert opinion: The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction. 1. Introduction inhibition prediction is extremely crucial for drug toxicity and drug-drug interactions predictions in drug discovery. A shocking 90% attrition rate of drug candidates is reported Combining the efforts of molecular dynamics (MD) simulations by the pharmaceutical industry during the transition from and in silico prediction for CYP inhibition has improved the preclinical trials to marketing surveillance trials or phase 4 safety of the drugs up to many folds [8]. clinical trials after spending an estimated US$ 2.6 billion for each new chemical entity (NCE) [1–3]. The US Food and Drug The idea about ADMET parameters for each drug is to have Administration (FDA) has reported that only 12 novel small a significant impact before entering preclinical trials to reduce molecule drugs [2,4] and 59 NCE (comprised of 64% small the withdrawal of the drug from a certain stage of pre-clinical molecule drugs) were approved in the years 2016 and 2018, and clinical trials. Therefore, most of the pharmaceutical indus­ respectively. Undesirable bioavailability of drugs due to impro­ tries rely heavily on the earlier evaluation through in silico per pharmacokinetic (PK) and pharmacodynamic (PD) proper­ prediction tools that comprises tools such as regression and ties, followed by toxicity, are the key reasons for the high classification-based approaches, machine learning (ML) meth­ failure rate of drug discoveries. A fine balance between drug- ods as well as artificial intelligence (AI) [9]. In many cases, the candidates (the drugs to be) and their ADMET (absorption, prediction tools can be the combinations of all tools with distribution, metabolism, elimination, and toxicity) profiling, integrated databases of both the existing and approved during the synthesis of drug molecules, can help avoid late- drugs and the experimental drugs under screening. stage drug failure in the drug discovery process. Thus, the A wide range of tools are available, where a few are com­ earlier detection of PK/PD properties along with drug- mercial, and a few are open access ones (in majority cases likeness and ADMET profiling can save both money and online in silico tools). Such commercial tools as CASE ULTRA time, ensuring simultaneously the safety and stability of the [10], DEREK [11], META-PC [12], METEOR [13], PASS [14], GUSAR designed-drugs or candidate-drugs [5,6]. [15] etc. are available in the market for quick predictions The terms such as ‘drug-likeness,’ ‘PK/PD study,’ and through ADMET profiling. Under open access tools, online ‘ADMET profiling’ are addressed by researchers interchange­ servers such as ADMETlab [16], admetSAR [17], pkCSM [18], ably many times, but they are different from each other and SwissADME [19], etc. are quite popular among researchers for each has a crucial role to play in drug discovery [7]. The ideas a fast and money-saving prediction of ADMET, followed by about PK/PD, drug-likeness, lipophilicity, water solubility, and a rational synthesis of a probable drug molecule instead of toxicity are considered under the single term of ADMET profil­ blind synthesis of any drug-candidate. The open access tools/ ing in a broader sense. In the profiling of drug metabolism in silico tools and computer coding are breaking the old-age (under ADMET), human cytochrome P450 (CYP450) enzyme commercialization and monopoly of pharmaceutical CONTACT Supratik Kar [email protected]; Jerzy Leszczynski [email protected] Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA © 2020 Informa UK Limited, trading as Taylor & Francis Group 2 S. KAR AND J. LESZCZYNSKI properties, drug-likeness score, ADMET properties, and ecotoxi­ Article highlights city endpoints in Table 1. Here we have only demonstrated the most common properties. However, the list can continue to ● Fine-tuning the balance in between drug-likeness and ADMET (absorption, distribution, metabolism, elimination, and toxicity), dur­ grow, depending on the nature of the drug and its target. ing the synthesis of drug molecules, can help avoid late-stage failure Major in silico tools implemented to model these properties of the candidate-drug in the process. (endpoints) are also reported in Table 1. ● In silico tools are capable of predicting the drug-likeness and ADMET profiling of drug candidates, even before their practical synthesis, helps minimize the costs involving synthesis, preclinical and clinical studies. ● Open access databases with integrated drug-likeness and ADMET 3. Application of in silico tools in drug discovery and profiling are the key-sources for in silico modeling and one of the primary resources for making in silico tools. ADMET ● The accuracy of ADMET prediction of an in silico tool depends on the quality of the experimental data under the database, the implemen­ In silico tools such as regression, classification based-QSAR, ML ted in silico tools to model them, stringent validation criteria and the techniques, combinatorial chemistry along with pharmacophore, idea of applicability domain of the developed model. docking, and molecular dynamics approaches have helped ● User friendliness, the output type (qualitative or quantitative), easy interpretation of the obtained results, and the user’s study require­ achieving impeccable impression in drug designing [20] and ments are the major criteria for choosing in silico tools. new catalyst designing [40], with substantial capacity in the ● For reliable prediction, users should use multiple in silico tools for field of reaction pathway prediction [41]. Over the last two prediction purposes and compare the results, which are to be fol­ lowed by identifying the most probable or similar prediction among decades, the literature and predictive in silico tools sufficiently the in silico tools. inform that well-developed prediction models have been able to predict the ADMET profiling and drug-likeness long before drug’s This box summarizes key points contained in the article. real synthesis [3,5,42]. ADMETlab [16], admetSAR [17], SwissADME [19], FAF-Drugs [43], TOPKAT [44] are some impor­ tant in silico tools that are most frequently used by academicians and industries. Apart from that the transformation products of industries. These open access in silico tools are fully integrated drugs in the human body as well as the biodegradation products with ADMET prediction platforms, including multiple quanti­ of these drugs in different environmental systems can be pre­ tative-structure-activity-relationships (QSAR) and ML models cisely predicted through the developed models included in that are capable of excluding undesirable drug candidates META-PC and Meteor-Nexus. The site of metabolism and the based on ADMET, reducing the number of synthesis and the type of enzymes involved in the biotransformation of the drugs degree of failures in preclinical and late-stage clinical trials can be identified with the development of computerized models [20,21]. The in silico tools are capable of predicting ADMET, (CypReact [45], XenoSite [46]).