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 29 publications

(16 citation statements)

References 44 publications

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“…Another needed advancement for cancer combination therapy prediction is methods that can be used to monitor disease progression and response to treatment over time, such as Eduati et al's approach, which utilizes microfluidics and logic-based models to predict treatments for different stages of pancreatic cancer [72]. Other considerations when developing new drug combination prediction models include the different interactions that can occur between drug combinations across drug dosages [29,73,74], the specificity the predicted drug regimens have for the disease over normal tissue [75][76][77], as well as increased emphasis on prioritizing candidate regimens with maximal efficacy and minimal toxicity, as most current studies attempt to maximize the synergy of drug combination without regard to the fact that this may compound toxicity as well, reducing the tolerability and clinical utility of the proposed therapy [78]. As computational methods improve to better incorporate patient-derived multi-omics data, disease-specific context, and pharmacodynamic considerations, more comprehensive models can be generated to predict effective drug regimens for complex diseases like cancer, reducing drug development time and cost and improving patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

Computational Advancements in Cancer Combination Therapy Prediction

Flanary,

Fisher,

Wilk

et al. 2023

JCO Precision Oncology

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Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.

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Section: Discussionmentioning

confidence: 99%

Computational Advancements in Cancer Combination Therapy Prediction

Flanary,

Fisher,

Wilk

et al. 2023

JCO Precision Oncology

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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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“…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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Systematic review of computational methods for drug combination prediction

Cited by 29 publications

References 44 publications

Computational Advancements in Cancer Combination Therapy Prediction

Computational Advancements in Cancer Combination Therapy Prediction

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

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