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Paliwal A, Jain S, Kumar S, Wal P, Khandai M, Khandige PS, Sadananda V, Anwer MK, Gulati M, Behl T, Srivastava S. Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine. Expert Opin Drug Metab Toxicol 2024; 20:181-195. [PMID: 38480460 DOI: 10.1080/17425255.2024.2330666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024]
Abstract
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.
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Affiliation(s)
- Ajita Paliwal
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
| | - Smita Jain
- Department of Pharmacy, Banasthali Vidyapith, Banasthali, India
| | - Sachin Kumar
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India
| | - Pranay Wal
- Department of Pharmacy, Pranveer Singh Institute of Technology, Pharmacy, Kanpur, India
| | - Madhusmruti Khandai
- Department of Pharmacy, Royal College of Pharmacy and Health Sciences, Berahmpur, India
| | - Prasanna Shama Khandige
- NGSM Institute of Pharmaceutical Sciences, Department of Pharmacology, Manglauru, NITTE (Deemed to be University), Manglauru, India
| | - Vandana Sadananda
- AB Shetty Memorial Institute of Dental Sciences, Department of Conservative Dentistry and Endodontics, NITTE (Deemed to be University), Mangaluru, India
| | - Md Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Monica Gulati
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India
- ARCCIM, Health, University of Technology, Sydney, Ultimo, Australia
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Mohali, Punjab, India
| | - Shriyansh Srivastava
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India
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2
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Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem 2020; 8:726. [PMID: 33062633 PMCID: PMC7517894 DOI: 10.3389/fchem.2020.00726] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022] Open
Abstract
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.
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Affiliation(s)
- Fengxu Wu
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China
| | - Yuquan Zhou
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Langhui Li
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianhuan Shen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Ganying Chen
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Xiaoqing Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Xianyang Liang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
| | - Mengyuan Tan
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Dongguan, China
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, China
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3
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Raevsky OA, Grigorev VY, Polianczyk DE, Raevskaja OE, Dearden JC. Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict? Mini Rev Med Chem 2019; 19:362-372. [PMID: 30058484 DOI: 10.2174/1389557518666180727164417] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 07/06/2018] [Accepted: 07/20/2018] [Indexed: 01/07/2023]
Abstract
Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models.
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Affiliation(s)
- Oleg A Raevsky
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russian Federation
| | - Veniamin Y Grigorev
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russian Federation
| | - Daniel E Polianczyk
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russian Federation
| | - Olga E Raevskaja
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russian Federation
| | - John C Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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4
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Sun J, Carlsson L, Ahlberg E, Norinder U, Engkvist O, Chen H. Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets. J Chem Inf Model 2017. [PMID: 28628322 DOI: 10.1021/acs.jcim.7b00159] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.
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Affiliation(s)
| | | | | | - Ulf Norinder
- Swetox, Karolinska Institutet , Unit of Toxicology Sciences, Södertälje 15136, Sweden
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5
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Miteva MA, Villoutreix BO. Computational Biology and Chemistry in MTi: Emphasis on the Prediction of Some ADMET Properties. Mol Inform 2017; 36. [DOI: 10.1002/minf.201700008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 02/03/2017] [Indexed: 12/21/2022]
Affiliation(s)
- Maria A. Miteva
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques In Silico , Inserm UMR−S 973; 35 rue Helene Brion 75013 Paris France
- INSERM, U973; F-75205 Paris France
| | - Bruno O. Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques In Silico , Inserm UMR−S 973; 35 rue Helene Brion 75013 Paris France
- INSERM, U973; F-75205 Paris France
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6
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He SB, Ben Hu, Kuang ZK, Wang D, Kong DX. Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D). Sci Rep 2016; 6:36595. [PMID: 27812030 PMCID: PMC5095671 DOI: 10.1038/srep36595] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 10/18/2016] [Indexed: 02/02/2023] Open
Abstract
Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r2) of the regression models was 0.664 (for test sets). The 2B-3 (A2Bvs A3) model performed best with q2 = 0.769 for training sets (10-fold cross-validation), and r2 = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A2Avs A3) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.
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Affiliation(s)
- Song-Bing He
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.,College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ben Hu
- Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zheng-Kun Kuang
- Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Dong Wang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - De-Xin Kong
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.,Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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7
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Winkler DA, Le TC. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR. Mol Inform 2016; 36. [DOI: 10.1002/minf.201600118] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 10/04/2016] [Indexed: 12/17/2022]
Affiliation(s)
- David A. Winkler
- CSIRO Manufacturing; Clayton 3168 Australia
- Monash Institute of Pharmaceutical Sciences; Monash University; Parkville 3052 Australia
- Latrobe Institute for Molecular Science; Latrobe University; Bundoora 3082 Australia
- School of Chemical and Physical Science; Flinders University; Bedford Park 5042 Australia
| | - Tu C. Le
- CSIRO Manufacturing; Clayton 3168 Australia
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8
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Raevsky OA, Polianczyk DE, Grigorev VY, Raevskaja OE, Dearden JC. In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models. Mol Inform 2015; 34:417-30. [PMID: 27490387 DOI: 10.1002/minf.201400144] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 03/05/2015] [Indexed: 11/07/2022]
Abstract
32 Quantitative Structure-Property Relationship (QSPR) models were constructed for prediction of aqueous intrinsic solubility of liquid and crystalline chemicals. Data sets contained 1022 liquid and 2615 crystalline compounds. Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF) methods were used to construct global models, and k-nearest neighbour (kNN), Arithmetic Mean Property (AMP) and Local Regression Property (LoReP) were used to construct local models. A set of the best QSPR models was obtained: for liquid chemicals with RMSE (root mean square error) of prediction in the range 0.50-0.60 log unit; for crystalline chemicals 0.80-0.90 log unit. In the case of global models the large number of descriptors makes mechanistic interpretation difficult. The local models use only one or two descriptors, so that a medicinal chemist working with sets of structurally-related chemicals can readily estimate their solubility. However, construction of stable local models requires the presence of closely related neighbours for each chemical considered. It is probable that a consensus of global and local QSPR models will be the optimal approach for construction of stable predictive QSPR models with mechanistic interpretation.
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Affiliation(s)
- Oleg A Raevsky
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, 142432, Russia, Chernogolovka, Severniy proezd 1 phone: +7 496 52 21867.
| | - Daniel E Polianczyk
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, 142432, Russia, Chernogolovka, Severniy proezd 1 phone: +7 496 52 21867
| | - Veniamin Yu Grigorev
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, 142432, Russia, Chernogolovka, Severniy proezd 1 phone: +7 496 52 21867
| | - Olga E Raevskaja
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, 142432, Russia, Chernogolovka, Severniy proezd 1 phone: +7 496 52 21867
| | - John C Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
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9
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Sheridan RP, McMasters DR, Voigt JH, Wildey MJ. eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk. J Chem Inf Model 2015; 55:231-8. [DOI: 10.1021/ci500666m] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Robert P. Sheridan
- Structural
Chemistry, Merck Research Laboratories, P.O. Box 2000, Rahway, New
Jersey 07065, United States
| | - Daniel R. McMasters
- Structural
Chemistry, Merck Research Laboratories, 33 Avenue Louis Pasteur, Boston, Massachusetts 02115, United States
| | - Johannes H. Voigt
- Structural
Chemistry, Merck Research Laboratories, P.O. Box 2000, Rahway, New
Jersey 07065, United States
| | - Mary Jo Wildey
- In Vitro
Pharmacology, Merck Research Laboratories, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
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10
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Visconti A, Ermondi G, Caron G, Esposito R. Prediction and interpretation of the lipophilicity of small peptides. J Comput Aided Mol Des 2015; 29:361-70. [PMID: 25577035 DOI: 10.1007/s10822-015-9829-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 01/02/2015] [Indexed: 01/28/2023]
Abstract
Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict [Formula: see text] of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict [Formula: see text] of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.
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Affiliation(s)
- Alessia Visconti
- Department of Genomics of Common Disease, Imperial College London, Du Cane Road, W12 ONN, London, UK,
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11
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Romero L, Vela JM. Alternative Models in Drug Discovery and Development Part I:In SilicoandIn VitroModels. ACTA ACUST UNITED AC 2014. [DOI: 10.1002/9783527679348.ch02] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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12
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In silico mechanistic profiling to probe small molecule binding to sulfotransferases. PLoS One 2013; 8:e73587. [PMID: 24039991 PMCID: PMC3765257 DOI: 10.1371/journal.pone.0073587] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Accepted: 07/28/2013] [Indexed: 01/01/2023] Open
Abstract
Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.
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13
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Benigni R, Battistelli CL, Bossa C, Colafranceschi M, Tcheremenskaia O. Mutagenicity, carcinogenicity, and other end points. Methods Mol Biol 2013; 930:67-98. [PMID: 23086838 DOI: 10.1007/978-1-62703-059-5_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
Aiming at understanding the structural and physical chemical basis of the biological activity of chemicals, the science of structure-activity relationships has seen dramatic progress in the last decades. Coarse-grain, qualitative approaches (e.g., the structural alerts), and fine-tuned quantitative structure-activity relationship models have been developed and used to predict the toxicological properties of untested chemicals. More recently, a number of approaches and concepts have been developed as support to, and corollary of, the structure-activity methods. These approaches (e.g., chemical relational databases, expert systems, software tools for manipulating the chemical information) have dramatically expanded the reach of the structure-activity work; at present, they are powerful and inescapable tools for computer chemists, toxicologists, and regulators. This chapter, after a general overview of traditional and well-known approaches, gives a detailed presentation of the latter more recent support tools freely available in the public domain.
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Affiliation(s)
- Romualdo Benigni
- Environment and Health Department, Istitituto Superiore di Sanita', Rome, Italy.
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14
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Palacios-Bejarano B, Cerruela García G, Luque Ruiz I, Gómez-Nieto MÁ. QSAR model based on weighted MCS trees approach for the representation of molecule data sets. J Comput Aided Mol Des 2013; 27:185-201. [DOI: 10.1007/s10822-013-9637-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Accepted: 02/01/2013] [Indexed: 11/28/2022]
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15
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Szaleniec M. Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates. Pharmacol Rep 2012; 64:761-81. [DOI: 10.1016/s1734-1140(12)70873-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2012] [Revised: 04/16/2012] [Indexed: 11/26/2022]
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16
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Moda TL, Andricopulo AD. Consensus hologram QSAR modeling for the prediction of human intestinal absorption. Bioorg Med Chem Lett 2012; 22:2889-93. [DOI: 10.1016/j.bmcl.2012.02.061] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 02/16/2012] [Accepted: 02/17/2012] [Indexed: 11/28/2022]
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17
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Chen B, Sheridan RP, Hornak V, Voigt JH. Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions. J Chem Inf Model 2012; 52:792-803. [DOI: 10.1021/ci200615h] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Bin Chen
- School of Informatics
and Computing, Indiana University at Bloomington, Bloomington, Indiana 47405, United States
| | - Robert P. Sheridan
- Chemistry Modeling and Informatics
Department, Merck Research Laboratories, Rahway, New Jersey 07065, United States
| | - Viktor Hornak
- Chemistry Modeling and Informatics
Department, Merck Research Laboratories, Rahway, New Jersey 07065, United States
| | - Johannes H. Voigt
- Chemistry Modeling and Informatics
Department, Merck Research Laboratories, Rahway, New Jersey 07065, United States
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18
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Modi S, Hughes M, Garrow A, White A. The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries. Drug Discov Today 2012; 17:135-42. [DOI: 10.1016/j.drudis.2011.10.022] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2011] [Revised: 10/07/2011] [Accepted: 10/19/2011] [Indexed: 10/15/2022]
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19
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Moroy G, Martiny VY, Vayer P, Villoutreix BO, Miteva MA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today 2011; 17:44-55. [PMID: 22056716 DOI: 10.1016/j.drudis.2011.10.023] [Citation(s) in RCA: 167] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2011] [Revised: 10/07/2011] [Accepted: 10/21/2011] [Indexed: 12/12/2022]
Abstract
Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.
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Affiliation(s)
- Gautier Moroy
- Inserm UMR-S 973, Molécules Thérapeutiques In Silico, Université Paris Diderot, Sorbonne Paris Cité, 35 Rue Helene Brion, 75013 Paris, France
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Hammann F, Suenderhauf C, Huwyler J. A binary ant colony optimization classifier for molecular activities. J Chem Inf Model 2011; 51:2690-6. [PMID: 21854036 DOI: 10.1021/ci200186m] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Chemical fingerprints encode the presence or absence of molecular features and are available in many large databases. Using a variation of the Ant Colony Optimization (ACO) paradigm, we describe a binary classifier based on feature selection from fingerprints. We discuss the algorithm and possible cross-validation procedures. As a real-world example, we use our algorithm to analyze a Plasmodium falciparum inhibition assay and contrast its performance with other machine learning paradigms in use today (decision tree induction, random forests, support vector machines, artificial neural networks). Our algorithm matches established paradigms in predictive power, yet supplies the medicinal chemist and basic researcher with easily interpretable results. Furthermore, models generated with our paradigm are easy to implement and can complement virtual screenings by additionally exploiting the precalculated fingerprint information.
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Affiliation(s)
- Felix Hammann
- Division of Pharmaceutical Technology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50 4056, Basel, Switzerland.
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21
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Shim J, MacKerell AD. Computational ligand-based rational design: Role of conformational sampling and force fields in model development. MEDCHEMCOMM 2011; 2:356-370. [PMID: 21716805 PMCID: PMC3123535 DOI: 10.1039/c1md00044f] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.
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22
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Cheng F, Shen J, Yu Y, Li W, Liu G, Lee PW, Tang Y. In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods. CHEMOSPHERE 2011; 82:1636-43. [PMID: 21145574 DOI: 10.1016/j.chemosphere.2010.11.043] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2010] [Revised: 11/08/2010] [Accepted: 11/16/2010] [Indexed: 05/12/2023]
Abstract
There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods.
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Affiliation(s)
- Feixiong Cheng
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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23
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Benigni R, Bossa C. Mechanisms of Chemical Carcinogenicity and Mutagenicity: A Review with Implications for Predictive Toxicology. Chem Rev 2011; 111:2507-36. [PMID: 21265518 DOI: 10.1021/cr100222q] [Citation(s) in RCA: 239] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Romualdo Benigni
- Istituto Superiore di Sanita’, Environment and Health Department, Viale Regina Elena, 299 00161 Rome, Italy
| | - Cecilia Bossa
- Istituto Superiore di Sanita’, Environment and Health Department, Viale Regina Elena, 299 00161 Rome, Italy
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