1
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Danel T, Wojtuch A, Podlewska S. Generation of new inhibitors of selected cytochrome P450 subtypes- In silico study. Comput Struct Biotechnol J 2022; 20:5639-5651. [PMID: 36284709 PMCID: PMC9582735 DOI: 10.1016/j.csbj.2022.10.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 11/16/2022] Open
Abstract
Physicochemical and pharmacokinetic compound profile has crucial impact on compound potency to become a future drug. Ligands with desired activity profile cannot be used for treatment if they are characterized by unfavourable physicochemical or ADMET properties. In the study, we consider metabolic stability and focus on selected subtypes of cytochrome P450 - proteins, which take part in the first phase of compound transformations in the organism. We develop a protocol for generation of new potential inhibitors of selected cytochrome isoforms. Its subsequent stages are composed of generation and assessment of new derivatives of known cytochrome inhibitors, docking and evaluation of the compound possible inhibition on the basis of the obtained ligand-protein complexes. Besides the library of new potential agents inhibiting particular cytochrome subtypes, we also prepare a graph neural network that predicts the change in activity for all modifications of the starting molecule. In addition, we perform a systematic statistical study on the influence of particular substitutions on the potential inhibition properties of generated compounds (both mono- and di-substitutions are considered), provide explanations of the inhibitory predictions and prepare an on-line visualization platform enabling manual inspection of the results. The developed methodology can greatly support the design of new cytochrome P450 inhibitors with the overarching goal of generation of new metabolically stable compounds. It enables instant evaluation of possible compound-cytochrome interactions and selection of ligands with the highest potential of possessing desired biological activity.
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Key Words
- CYP inhibitors
- CYP, cytochrome P450
- CYP450
- DL, deep learning
- DNNs, deep neural networks
- Docking
- Explainability
- GNN, graph neural network
- Graph neural networks
- ML, machine learning
- MSE, mean squared error
- Morgan FP, Morgan fingerprint
- New compounds generation
- On-line platform
- QSPR, quantitative structure-property relationship
- RF, random forest
- SRD, sum of ranking differences
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Affiliation(s)
- Tomasz Danel
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Agnieszka Wojtuch
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Department of Medicinal Chemistry, 31-343 Kraków, Smętna Street 12, Poland,Corresponding author.
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2
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Mughal H, Bell EC, Mughal K, Derbyshire ER, Freundlich JS. Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents. ACS Infect Dis 2022; 8:1553-1562. [PMID: 35894649 PMCID: PMC9987178 DOI: 10.1021/acsinfecdis.2c00189] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The need for novel antimalarials is apparent given the continuing disease burden worldwide, despite significant drug discovery advances from the bench to the bedside. In particular, small-molecule agents with potent efficacy against both the liver and blood stages of Plasmodium parasite infection are critical for clinical settings as they would simultaneously prevent and treat malaria with a reduced selection pressure for resistance. While experimental screens for such dual-stage inhibitors have been conducted, the time and cost of these efforts limit their scope. Here, we have focused on leveraging machine learning approaches to discover novel antimalarials with such properties. A random forest modeling approach was taken to predict small molecules with in vitro efficacy versus liver-stage Plasmodium berghei parasites and a lack of human liver cell cytotoxicity. Empirical validation of the model was achieved with the realization of hits with liver-stage efficacy after prospective scoring of a commercial diversity library and consideration of structural diversity. A subset of these hits also demonstrated promising blood-stage Plasmodium falciparum efficacy. These 18 validated dual-stage antimalarials represent novel starting points for drug discovery and mechanism of action studies with significant potential for seeding a new generation of therapies.
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Affiliation(s)
- Haseeb Mughal
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University – New Jersey Medical School, 185 South Orange Ave, Newark, NJ, 07103
| | - Elise C. Bell
- Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27708, USA
| | - Khadija Mughal
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University – New Jersey Medical School, 185 South Orange Ave, Newark, NJ, 07103
| | - Emily R. Derbyshire
- Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27708, USA
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, 213 Research Drive, Durham, NC 27710, USA
| | - Joel S. Freundlich
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University – New Jersey Medical School, 185 South Orange Ave, Newark, NJ, 07103
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University – New Jersey Medical School, Newark, NJ, 07103
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3
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Wojtuch A, Jankowski R, Podlewska S. How can SHAP values help to shape metabolic stability of chemical compounds? J Cheminform 2021; 13:74. [PMID: 34579792 PMCID: PMC8477573 DOI: 10.1186/s13321-021-00542-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 08/15/2021] [Indexed: 12/31/2022] Open
Abstract
Background Computational methods support nowadays each stage of drug design campaigns. They assist not only in the process of identification of new active compounds towards particular biological target, but also help in the evaluation and optimization of their physicochemical and pharmacokinetic properties. Such features are not less important in terms of the possible turn of a compound into a future drug than its desired affinity profile towards considered proteins. In the study, we focus on metabolic stability, which determines the time that the compound can act in the organism and play its role as a drug. Due to great complexity of xenobiotic transformation pathways in the living organisms, evaluation and optimization of metabolic stability remains a big challenge. Results Here, we present a novel methodology for the evaluation and analysis of structural features influencing metabolic stability. To this end, we use a well-established explainability method called SHAP. We built several predictive models and analyse their predictions with the SHAP values to reveal how particular compound substructures influence the model’s prediction. The method can be widely applied by users thanks to the web service, which accompanies the article. It allows a detailed analysis of SHAP values obtained for compounds from the ChEMBL database, as well as their determination and analysis for any compound submitted by a user. Moreover, the service enables manual analysis of the possible structural modifications via the provision of analogous analysis for the most similar compound from the ChEMBL dataset. Conclusions To our knowledge, this is the first attempt to employ SHAP to reveal which substructural features are utilized by machine learning models when evaluating compound metabolic stability. The accompanying web service for metabolic stability evaluation can be of great help for medicinal chemists. Its significant usefulness is related not only to the possibility of assessing compound stability, but also to the provision of information about substructures influencing this parameter. It can assist in the design of new ligands with improved metabolic stability, helping in the detection of privileged and unfavourable chemical moieties during stability optimization. The tool is available at https://metstab-shap.matinf.uj.edu.pl/.
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Affiliation(s)
- Agnieszka Wojtuch
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 S. Łojasiewicza Street, 30-348, Kraków, Poland
| | - Rafał Jankowski
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 S. Łojasiewicza Street, 30-348, Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343, Kraków, Poland. .,Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, 9 Medyczna Street, 30-688, Kraków, Poland.
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4
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Patel JS, Norambuena J, Al-Tameemi H, Ahn YM, Perryman AL, Wang X, Daher SS, Occi J, Russo R, Park S, Zimmerman M, Ho HP, Perlin DS, Dartois V, Ekins S, Kumar P, Connell N, Boyd JM, Freundlich JS. Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-Resistant Staphylococcus aureus. ACS Infect Dis 2021; 7:2508-2521. [PMID: 34342426 DOI: 10.1021/acsinfecdis.1c00265] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
We present the application of Bayesian modeling to identify chemical tools and/or drug discovery entities pertinent to drug-resistant Staphylococcus aureus infections. The quinoline JSF-3151 was predicted by modeling and then empirically demonstrated to be active against in vitro cultured clinical methicillin- and vancomycin-resistant strains while also exhibiting efficacy in a mouse peritonitis model of methicillin-resistant S. aureus infection. We highlight the utility of an intrabacterial drug metabolism (IBDM) approach to probe the mechanism by which JSF-3151 is transformed within the bacteria. We also identify and then validate two mechanisms of resistance in S. aureus: one mechanism involves increased expression of a lipocalin protein, and the other arises from the loss of function of an azoreductase. The computational and experimental approaches, discovery of an antibacterial agent, and elucidated resistance mechanisms collectively hold promise to advance our understanding of therapeutic regimens for drug-resistant S. aureus.
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Affiliation(s)
- Jimmy S. Patel
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - Javiera Norambuena
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, United States
| | - Hassan Al-Tameemi
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, United States
| | - Yong-Mo Ahn
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - Alexander L. Perryman
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - Xin Wang
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - Samer S. Daher
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
| | - James Occi
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Riccardo Russo
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Steven Park
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - Matthew Zimmerman
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - Hsin-Pin Ho
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - David S. Perlin
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - Véronique Dartois
- Public Health Research Institute, Rutgers University − New Jersey Medical School, 225 Warren St, Newark, New Jersey 07103, United States
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
| | - Pradeep Kumar
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Nancy Connell
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Jeffrey M. Boyd
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, United States
| | - Joel S. Freundlich
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University − New Jersey Medical School, 185 South Orange Ave, Newark, New Jersey 07103, United States
- Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University − New Jersey Medical School, Newark, New Jersey 07103, United States
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5
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Mughal H, Wang H, Zimmerman M, Paradis MD, Freundlich JS. Random Forest Model Prediction of Compound Oral Exposure in the Mouse. ACS Pharmacol Transl Sci 2021; 4:338-343. [PMID: 33615183 DOI: 10.1021/acsptsci.0c00197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Indexed: 11/29/2022]
Abstract
An early hurdle in the optimization of small-molecule chemical probes and drug discovery entities is the attainment of sufficient exposure in the mouse via oral administration of the compound. While computational approaches have attempted to predict molecular properties related to the mouse pharmacokinetic (PK) profile, we present herein a machine learning approach to specifically predict the oral exposure of a compound as measured in the mouse snapshot PK assay. A random forest workflow was found to produce the best cross-validation and external test set statistics after processing of the input data set and optimization of model features. The modeling approach should be useful to the chemical biology and drug discovery communities to predict this key molecular property and afford chemical entities of translational significance.
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Affiliation(s)
- Haseeb Mughal
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University - New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Han Wang
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, New Jersey 07110, United States
| | - Matthew Zimmerman
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, New Jersey 07110, United States
| | - Marc D Paradis
- Holdings & Ventures, Northwell Health, Manhasset, New York 11030, United States
| | - Joel S Freundlich
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University - New Jersey Medical School, Newark, New Jersey 07103, United States.,Division of Infectious Disease, Department of Medicine and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University - New Jersey Medical School, Newark, New Jersey 07103, United States
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6
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Perryman A, Inoyama D, Patel JS, Ekins S, Freundlich JS. Pruned Machine Learning Models to Predict Aqueous Solubility. ACS OMEGA 2020; 5:16562-16567. [PMID: 32685821 PMCID: PMC7364544 DOI: 10.1021/acsomega.0c01251] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 05/13/2020] [Indexed: 05/03/2023]
Abstract
Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous solubility data were used to create two full, or nonpruned, training sets. These two sets were also combined to create a full fused set, and a training set comprised of a literature collation of solubility data was also considered as a reference. We tested different extents of data pruning on the training sets and constructed machine learning models that were evaluated with two independent, external test sets that contained compounds that were different from the training sets. The best pruned and fused model was significantly more accurate, in comparison to either the full model or the full fused model, with the prediction of these external test sets. By carefully removing data from the training set, less information can be used to create more accurate machine learning models for aqueous solubility. This knowledge and the curated training sets should prove useful to future machine learning approaches.
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Affiliation(s)
- Alexander
L. Perryman
- Department
of Pharmacology, Physiology, and Neuroscience, Rutgers University—New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Daigo Inoyama
- Department
of Pharmacology, Physiology, and Neuroscience, Rutgers University—New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Jimmy S. Patel
- Department
of Pharmacology, Physiology, and Neuroscience, Rutgers University—New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Sean Ekins
- Collaborations
in Chemistry, Inc., 5616
Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
| | - Joel S. Freundlich
- Department
of Pharmacology, Physiology, and Neuroscience, Rutgers University—New Jersey Medical School, Newark, New Jersey 07103, United States
- Division
of Infectious Disease, Department of Medicine and the Ruy V. Lourenço
Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University—New Jersey Medical School, Newark, New Jersey 07103, United States
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7
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Pereira JC, Daher SS, Zorn KM, Sherwood M, Russo R, Perryman AL, Wang X, Freundlich MJ, Ekins S, Freundlich JS. Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae. Pharm Res 2020; 37:141. [PMID: 32661900 DOI: 10.1007/s11095-020-02876-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 07/06/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology. METHODS Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >105 drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits. RESULTS Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae. CONCLUSIONS This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web. Graphical Abstract.
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Affiliation(s)
- Janaina Cruz Pereira
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA
| | - Samer S Daher
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Matthew Sherwood
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA
| | - Riccardo Russo
- Division of Infectious Disease, Department of Medicine and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA
| | - Alexander L Perryman
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA.,Repare Therapeutics,, 7210 Rue Frederick-Banting Suite 100, Montreal, QC, H4S 2A1, Canada
| | - Xin Wang
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Madeleine J Freundlich
- Stuart Country Day School of the Sacred Heart, 1200 Stuart Road, Princeton, NJ, 08540, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.,Collaborations in Chemistry, Inc. 5616 Hilltop Needmore Road, Fuquay-, Varina, NC, 27526, USA
| | - Joel S Freundlich
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA. .,Division of Infectious Disease, Department of Medicine and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ, 07103, USA.
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8
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Shou WZ. Current status and future directions of high-throughput ADME screening in drug discovery. J Pharm Anal 2020; 10:201-208. [PMID: 32612866 PMCID: PMC7322755 DOI: 10.1016/j.jpha.2020.05.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/14/2020] [Accepted: 05/14/2020] [Indexed: 02/06/2023] Open
Abstract
During the last decade high-throughput in vitro absorption, distribution, metabolism and excretion (HT-ADME) screening has become an essential part of any drug discovery effort of synthetic molecules. The conduct of HT-ADME screening has been "industrialized" due to the extensive development of software and automation tools in cell culture, assay incubation, sample analysis and data analysis. The HT-ADME assay portfolio continues to expand in emerging areas such as drug-transporter interactions, early soft spot identification, and ADME screening of peptide drug candidates. Additionally, thanks to the very large and high-quality HT-ADME data sets available in many biopharma companies, in silico prediction of ADME properties using machine learning has also gained much momentum in recent years. In this review, we discuss the current state-of-the-art practices in HT-ADME screening including assay portfolio, assay automation, sample analysis, data processing, and prediction model building. In addition, we also offer perspectives in future development of this exciting field.
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Affiliation(s)
- Wilson Z. Shou
- Bristol-Myers Squibb, PO Box 4000, Princeton, NJ, 08540, USA
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9
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Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP, Klein JJ, Hickey AJ, Clark AM. Exploiting machine learning for end-to-end drug discovery and development. NATURE MATERIALS 2019; 18:435-441. [PMID: 31000803 PMCID: PMC6594828 DOI: 10.1038/s41563-019-0338-z] [Citation(s) in RCA: 235] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 03/07/2019] [Indexed: 05/20/2023]
Abstract
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA.
| | - Ana C Puhl
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
| | | | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
| | - Daniel P Russo
- Collaborations Pharmaceuticals, Inc., Raleigh, NC, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA
| | | | - Anthony J Hickey
- RTI International, Research Triangle Park, NC, USA
- UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alex M Clark
- Molecular Materials Informatics, Inc., Montreal, Quebec, Canada
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10
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Ferreira LL, Andricopulo AD. ADMET modeling approaches in drug discovery. Drug Discov Today 2019; 24:1157-1165. [DOI: 10.1016/j.drudis.2019.03.015] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 02/08/2019] [Accepted: 03/14/2019] [Indexed: 12/31/2022]
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11
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Zhan P, Kang D, Liu X. Resurrecting the Condemned: Identification of N-Benzoxaborole Benzofuran GSK8175 as a Clinical Candidate with Reduced Metabolic Liability. J Med Chem 2019; 62:3251-3253. [PMID: 30916967 DOI: 10.1021/acs.jmedchem.9b00415] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Shandong University, 44 West Culture Road, 250012 Ji’nan, Shandong, P. R. China
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Shandong University, 44 West Culture Road, 250012 Ji’nan, Shandong, P. R. China
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Shandong University, 44 West Culture Road, 250012 Ji’nan, Shandong, P. R. China
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12
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Perryman AL, Patel JS, Russo R, Singleton E, Connell N, Ekins S, Freundlich JS. Naïve Bayesian Models for Vero Cell Cytotoxicity. Pharm Res 2018; 35:170. [PMID: 29959603 DOI: 10.1007/s11095-018-2439-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 06/05/2018] [Indexed: 11/30/2022]
Abstract
PURPOSE To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC50) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds. METHODS Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem. RESULTS Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors. CONCLUSIONS The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material. Graphical Abstract Naive Bayesian models have been trained with publically available data and offer a useful tool for chemical biology and drug discovery to select for small molecules with a high probability of exhibiting acceptably low Vero cell cytotoxicity.
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Affiliation(s)
- Alexander L Perryman
- Department of Pharmacology, Physiology and Neuroscience, and Medicine, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA
| | - Jimmy S Patel
- Department of Pharmacology, Physiology and Neuroscience, and Medicine, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA
| | - Riccardo Russo
- Division of Infectious Diseases, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA
| | - Eric Singleton
- Division of Infectious Diseases, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA
| | - Nancy Connell
- Division of Infectious Diseases, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., Main Campus Drive Lab 3510, Raleigh, North Carolina,, 27606, USA
| | - Joel S Freundlich
- Department of Pharmacology, Physiology and Neuroscience, and Medicine, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA. .,Division of Infectious Diseases, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave, Newark, NJ, 07103, USA.
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Dhiman R, Singh R. Recent advances for identification of new scaffolds and drug targets for Mycobacterium tuberculosis. IUBMB Life 2018; 70:905-916. [PMID: 29761628 DOI: 10.1002/iub.1863] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 04/07/2018] [Indexed: 02/06/2023]
Abstract
Tuberculosis (TB) is a leading cause of mortality and morbidity with an estimated 1.7 billion people latently infected with the pathogen worldwide. Clinically, TB infection presents itself as an asymptomatic infection, which gradually manifests to life threatening disease. The emergence of various drug resistant strains of Mycobacterium tuberculosis and lengthy duration of chemotherapy are major challenges in the field of TB drug development. Hence, there is an urgent need to develop scaffolds that possess a novel mechanism of action, can shorten the duration of therapy, and are active against both drug resistant and susceptible strains. In this review, we will discuss recent progress made in the field of TB drug development with emphasis on screening methods and drug targets from M. tuberculosis. The current review provides insights into mechanism of action of new scaffolds that are being evaluated in various stages of clinical trials. © 2018 IUBMB Life, 70(9):905-916, 2018.
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Affiliation(s)
- Rohan Dhiman
- Laboratory of Mycobacterial Immunology, Department of Life Science, National Institute of Technology, Rourkela, Odisha, India
| | - Ramandeep Singh
- Tuberculosis Research Laboratory, Vaccine and Infectious Disease Research Centre, Translational Health Science and Technology Institute, Haryana, India
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