Systematic review of computational methods for drug combination prediction

Zhu, Jie; Midena, Gianmarco; Chen, Yingjia; Athanasiadis, Paschalis; Wang, Tiandyanui; Rousu, Juho; He, Liye; Aittokallio, Tero

doi:10.1016/j.csbj.2022.05.055

Cited by 15 publications

(5 citation statements)

References 44 publications

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“…In addition, knowledge of intratumoral therapy vulnerability has been explored to inform formulation of drug combinations that target multiple cell groups to help eliminate heterogeneous tumors 20,59 . Given the vast number of potential combination therapies, computational frameworks have been proposed to conduct virtual systematic screens for specific indications 60,61 . To this end, cellular drug response scores are key components for modeling combination efficacies.…”

Section: Discussionmentioning

confidence: 99%

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Zhang,

Maeser,

Lee

et al. 2023

Preprint

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Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen’s d > 1.0). More importantly, we examine our method’s utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

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Section: Discussionmentioning

confidence: 99%

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Zhang,

Maeser,

Lee

et al. 2023

Preprint

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“…Computational methods played a crucial role in systematically screening combination effects in-silico, prioritizing potent combinations for further testing amid the vast number of potential options. A systematic literature review presented by Kong et al 13 encompassing 117 computational methods that classified these methods based on their combination prediction tasks and input data requirements to aid researchers in selecting appropriate prediction methods for diverse real-world applications. While most methods focused on predicting or classifying combination synergy, few considered the efficacy and potential toxicity, key determinants of therapeutic success.…”

Section: Related Workmentioning

confidence: 99%

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Abd El-Hafeez,

Shams,

Elshaier

et al. 2024

Sci Rep

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Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O’Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

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“…Finally, these algorithms assess the potential binding free energy, which works in conjunction with the scoring function to identify the molecules that have a higher propensity for binding to targets during molecular docking. There are four primary categories of scoring functions: I functions for consensus scoring, empirical scoring functions, knowledge-based scoring functions, and force-field-based scoring functions, among other options [ 88 , 112 ].…”

Section: Structure-based Approachmentioning

confidence: 99%

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

et al. 2022

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Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

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Systematic review of computational methods for drug combination prediction

Cited by 15 publications

References 44 publications

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

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