1
|
Murali V, Muralidhar YP, Königs C, Nair M, Madhu S, Nedungadi P, Srinivasa G, Athri P. Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning. Chem Biol Drug Des 2022; 100:169-184. [PMID: 35587730 DOI: 10.1111/cbdd.14092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 04/24/2022] [Accepted: 05/15/2022] [Indexed: 11/29/2022]
Abstract
The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications for costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. In the above list, biological activities have never been used as an independent variable towards the prediction of clinical trial outcomes. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the "Pass" class. "Pass" refers to one of the two classes (Pass/Fail) of all clinical trials, and the model performed well in predicting the "Pass" category. Through the analysis of feature contributions to predictive capability, we have demonstrated that bioactivity plays a statistically significant role in predicting clinical trial outcome. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. Cleaned, organized, integrated data and code to map these entities, created as a part of this work, are available open-source. This reproducibility and the freely available code ensure that researchers with access to deep curated and proprietary clinical trial databases (we only use open-source data in this study) can further expand the scope of the results.
Collapse
Affiliation(s)
- Vidhya Murali
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, India
| | - Y Pradyumna Muralidhar
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India
| | - Cassandra Königs
- Bioinformatics and Medical Informatics, Bielefeld University, Northrhine-Westphalia, Germany
| | - Meera Nair
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
| | - Sethulekshmi Madhu
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
| | - Prema Nedungadi
- Department of Computer Science and Engineering, Amrita School of Engineering, Kerala, India
| | - Gowri Srinivasa
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, India
| |
Collapse
|
2
|
Gimadiev T, Nugmanov R, Batyrshin D, Madzhidov T, Maeda S, Sidorov P, Varnek A. Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data. J Chem Inf Model 2021; 61:554-559. [PMID: 33502186 DOI: 10.1021/acs.jcim.0c01280] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Presently, quantum chemical calculations are widely used to generate extensive data sets for machine learning applications; however, generally, these sets only include information on equilibrium structures and some close conformers. Exploration of potential energy surfaces provides important information on ground and transition states, but analysis of such data is complicated due to the number of possible reaction pathways. Here, we present RePathDB, a database system for managing 3D structural data for both ground and transition states resulting from quantum chemical calculations. Our tool allows one to store, assemble, and analyze reaction pathway data. It combines relational database CGR DB for handling compounds and reactions as molecular graphs with a graph database architecture for pathway analysis by graph algorithms. Original condensed graph of reaction technology is used to store any chemical reaction as a single graph.
Collapse
Affiliation(s)
- Timur Gimadiev
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
| | - Ramil Nugmanov
- Laboratory of Chemoinformatics and Molecular Modeling, Butlerov Institute of Chemistry, Kazan Federal University, 18, Kremlyovskaya str., 420008 Kazan, Russia
| | - Dinar Batyrshin
- Laboratory of Chemoinformatics and Molecular Modeling, Butlerov Institute of Chemistry, Kazan Federal University, 18, Kremlyovskaya str., 420008 Kazan, Russia
| | - Timur Madzhidov
- Laboratory of Chemoinformatics and Molecular Modeling, Butlerov Institute of Chemistry, Kazan Federal University, 18, Kremlyovskaya str., 420008 Kazan, Russia
| | - Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
| | - Pavel Sidorov
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
| | - Alexandre Varnek
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan.,Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 4, Blaise Pascal str., 67081 Strasbourg, France
| |
Collapse
|
3
|
Shankar S, Bhandari I, Okou DT, Srinivasa G, Athri P. Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network. Chem Biol Drug Des 2020; 97:665-673. [PMID: 33006799 DOI: 10.1111/cbdd.13802] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 09/20/2020] [Indexed: 12/16/2022]
Abstract
Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from single drugs, only a few of them explore models that predict ADRs from drug combinations. Further, as far as we know, none of them have developed models using transcriptomic data, specifically the LINCS L1000 drug-induced gene expression data to predict ADRs for drug combinations. In this study, we use the TWOSIDES database as a source of ADRs originating from two-drug combinations. 34,549 common drug pairs between these two databases were used to train an artificial neural network (ANN), to predict 243 ADRs that were induced by at least 10% of the drug pairs. Our model predicts the occurrence of these ADRs with an average accuracy of 82% across a multifold cross-validation.
Collapse
Affiliation(s)
- Susmitha Shankar
- Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India
| | - Ishita Bhandari
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India
| | - David T Okou
- Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Gowri Srinivasa
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India
| |
Collapse
|