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Fatima I, Rehman A, Ding Y, Wang P, Meng Y, Rehman HU, Warraich DA, Wang Z, Feng L, Liao M. Breakthroughs in AI and multi-omics for cancer drug discovery: A review. Eur J Med Chem 2024; 280:116925. [PMID: 39378826 DOI: 10.1016/j.ejmech.2024.116925] [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: 06/28/2024] [Revised: 09/19/2024] [Accepted: 09/27/2024] [Indexed: 10/10/2024]
Abstract
Cancer is one of the biggest medical challenges we face today. It is characterized by abnormal, uncontrolled growth of cells that can spread to different parts of the body. Cancer is extremely complex, with genetic variations and the ability to adapt and evolve. This means we must continuously pursue innovative approaches to developing new cancer drugs. While traditional drug discovery methods have led to important breakthroughs, they also have significant limitations that make it difficult to efficiently create new, cost-effective cancer therapies. Integrating computational tools into the cancer drug discovery process is a major step forward. By harnessing computing power, we can overcome some of the inherent barriers of traditional methods. This review examines the range of computational techniques now being used, such as molecular docking, QSAR models, virtual screening, and pharmacophore modeling. It looks at recent advances in areas like machine learning and molecular simulations. The review also discusses the current challenges with these technologies and envisions future directions, underscoring how transformative these computational tools can be for creating targeted, new cancer treatments.
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Affiliation(s)
- Israr Fatima
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Abdur Rehman
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.
| | - Yanheng Ding
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Peng Wang
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Yuxuan Meng
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Hafeez Ur Rehman
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Dawood Ahmad Warraich
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Zhibo Wang
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Lijun Feng
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Mingzhi Liao
- Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.
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Curcio A, Rocca R, Alcaro S, Artese A. The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods. Pharmaceuticals (Basel) 2024; 17:620. [PMID: 38794190 PMCID: PMC11124352 DOI: 10.3390/ph17050620] [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: 04/18/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Histone deacetylases (HDACs) are crucial in gene transcription, removing acetyl groups from histones. They also influence the deacetylation of non-histone proteins, contributing to the regulation of various biological processes. Thus, HDACs play pivotal roles in various diseases, including cancer, neurodegenerative disorders, and inflammatory conditions, highlighting their potential as therapeutic targets. This paper reviews the structure and function of the four classes of human HDACs. While four HDAC inhibitors are currently available for treating hematological malignancies, numerous others are undergoing clinical trials. However, their non-selective toxicity necessitates ongoing research into safer and more efficient class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches, such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships, and structure-based virtual screening (molecular docking). Moreover, recent developments in the field of molecular dynamics simulations, combined with Poisson-Boltzmann/molecular mechanics generalized Born surface area techniques, have improved the prediction of ligand binding affinity. In this review, we delve into the ways in which these methods have contributed to designing and identifying HDAC inhibitors.
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Affiliation(s)
- Antonio Curcio
- Dipartimento di Scienze della Salute, Campus “S. Venuta”, Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy; (A.C.); (S.A.); (A.A.)
| | - Roberta Rocca
- Dipartimento di Scienze della Salute, Campus “S. Venuta”, Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy; (A.C.); (S.A.); (A.A.)
- Net4Science S.r.l., Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - Stefano Alcaro
- Dipartimento di Scienze della Salute, Campus “S. Venuta”, Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy; (A.C.); (S.A.); (A.A.)
- Net4Science S.r.l., Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - Anna Artese
- Dipartimento di Scienze della Salute, Campus “S. Venuta”, Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy; (A.C.); (S.A.); (A.A.)
- Net4Science S.r.l., Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
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Mondello A, Dal Bo M, Toffoli G, Polano M. Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges. Front Pharmacol 2024; 14:1260276. [PMID: 38264526 PMCID: PMC10803549 DOI: 10.3389/fphar.2023.1260276] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.
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Affiliation(s)
| | | | | | - Maurizio Polano
- Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Aviano, Italy
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Djokovic N, Rahnasto-Rilla M, Lougiakis N, Lahtela-Kakkonen M, Nikolic K. SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors. Pharmaceuticals (Basel) 2023; 16:ph16010127. [PMID: 36678624 PMCID: PMC9864763 DOI: 10.3390/ph16010127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/17/2023] Open
Abstract
A growing body of preclinical evidence recognized selective sirtuin 2 (SIRT2) inhibitors as novel therapeutics for treatment of age-related diseases. However, none of the SIRT2 inhibitors have reached clinical trials yet. Transformative potential of machine learning (ML) in early stages of drug discovery has been witnessed by widespread adoption of these techniques in recent years. Despite great potential, there is a lack of robust and large-scale ML models for discovery of novel SIRT2 inhibitors. In order to support virtual screening (VS), lead optimization, or facilitate the selection of SIRT2 inhibitors for experimental evaluation, a machine-learning-based tool titled SIRT2i_Predictor was developed. The tool was built on a panel of high-quality ML regression and classification-based models for prediction of inhibitor potency and SIRT1-3 isoform selectivity. State-of-the-art ML algorithms were used to train the models on a large and diverse dataset containing 1797 compounds. Benchmarking against structure-based VS protocol indicated comparable coverage of chemical space with great gain in speed. The tool was applied to screen the in-house database of compounds, corroborating the utility in the prioritization of compounds for costly in vitro screening campaigns. The easy-to-use web-based interface makes SIRT2i_Predictor a convenient tool for the wider community. The SIRT2i_Predictor's source code is made available online.
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Affiliation(s)
- Nemanja Djokovic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
- Correspondence: (N.D.); (K.N.)
| | - Minna Rahnasto-Rilla
- School of Pharmacy, University of Eastern Finland, P.O. Box 1627, 70210 Kuopio, Finland
| | - Nikolaos Lougiakis
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771 Athens, Greece
| | | | - Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia
- Correspondence: (N.D.); (K.N.)
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