Opportunities and challenges of machine learning in anticaner drug therapies

Abstract

Antitumor drug therapies encounter substantial costs and intricate challenges, imposing a financial strain on patients and potentially leading to serious adverse effects. These issues have prompted a shift towards personalized precision medicine, although the increased workload for clinicians limits its full implementation. Machine learning (ML) offers innovative solutions to these challenges. By effectively integrating and analysing large clinical datasets, ML can develop models to predict potential treatment-related risks for patients and optimize dosing regimens, thereby improving efficacy and reducing adverse effects. Additionally, ML can evaluate drug efficacy, providing empirical support for personalized treatments. This review highlights the research progress in ML for antitumor drug therapies and examines its crucial role in advancing personalized precision medicine. It is expected that ML will deliver more accurate, efficient, and cost-effective treatment options for patients while providing strong support for clinicians in refining treatment decisions, making it an essential tool in cancer therapy.

Keywords

Machine learning / Anti-cancer / Drug therapies / Precision medicine

References

[1] Dagogo-Jack I , Shaw AT . Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018; 15 (2): 81- 94.

[2] Sisodiya SM . Precision medicine and therapies of the future. Epilepsia. 2021; 62 (Suppl 2): S90- s105.

[3] Shotelersuk V , Tongsima S , Pithukpakorn M , et al. Precision medicine in Thailand. Am J Med Genet Part C, Seminars in medical genetics. 2019; 181 (2): 245- 253.

[4] Wang X . New strategies of clinical precision medicine. Clin Transl Med. 2022; 12 (2): e135.

[5] AL Kuwaiti A , Nazer K , AL-Reedy A , et al. A review of the role of artificial intelligence in healthcare. J Personalized Med. 2023; 13 (6).

[6] Zhou S , Sapp JL , Abdelwahab A , et al. Deep learning applied to electrocardiogram interpretation. Can J Cardiol. 2021; 37 (1): 17- 18.

[7] Jaradat AS , AL Mamlook RE , Almakayeel N , et al. Automated monkeypox skin lesion detection using deep learning and transfer learning techniques. Int J Environ Res Publ Health. 2023; 20 (5).

[8] Hammad M , Pławiak P , Elaffendi M , et al. Enhanced deep learning approach for accurate eczema and psoriasis skin detection. Sensors (Basel, Switzerland). 2023; 23 (16).

[9] Qaid TS , Mazaar H , AL-Shamri MYH , et al. Hybrid deep-learning and machinelearning models for predicting COVID-19. Comput Intell Neurosci. 2021: 20219996737.

[10] Wojtara M , Rana E , Rahman T , et al. Artificial intelligence in rare disease diagnosis and treatment. Clinical and translational science. 2023; 16 (11): 2106- 2111.

[11] Banerjee J , Taroni JN , Allaway RJ , et al. Machine learning in rare disease. Nat Methods. 2023; 20 (6): 803- 814.

[12] Shipeng Z , Pan M , Fang L . Application of machine learning in the therapeutic drug monitoring and individual drug therapy. China Pharm. 2023; 34 (1): 117- 121+28.

[13] Suqin F , Chenye H , Jun P . Artificial intelligence application in hospital pharmaceutical service: a bibliometric analysis. China Pharm. 2024; 35 (4): 494- 499.

[14] Haug CJ , Drazen JM . Artificial intelligence and machine learning in clinical medicine. N Engl J Med. 2023; 388 (13): 1201- 1208.

[15] Zhu J , Huang Q , Peng X , et al. Identification of molecular subtypes based on PANoptosis-related genes and construction of a signature for predicting the prognosis and response to immunotherapy response in hepatocellular carcinoma. Front Immunol. 2023: 141218661.

[16] Li J , Liang Y , Zhao X , et al. Integrating machine learning algorithms to systematically assess reactive oxygen species levels to aid prognosis and novel treatments for triple -negative breast cancer patients. Front Immunol. 2023: 141196054.

[17] Chen J , Hao L , Qian X , et al. Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients. Front Immunol. 2022: 13948601.

[18] Li X , Zhai Z , Ding W , et al. An Artificial Intelligence Model to Predict Survival and Chemotherapy Benefits for Gastric Cancer Patients after Gastrectomy Development and Validation in International Multicenter cohorts[J]. London, England: International journal of surgery; 2022: 105106889.

[19] Sasagawa S , Kato H , Nagaoka K , et al. Immuno-genomic profiling of biopsy specimens predicts neoadjuvant chemotherapy response in esophageal squamous cell carcinoma. Cell reports Medicine. 2022; 3 (8): 100705.

[20] Sun X , Guo P , Wang N , et al. A refined therapeutic plan based on the machinelearning prognostic model of liver hepatocellular carcinoma. Comput Biol Med. 2024: 169107907.

[21] Hwang M , Canzoniero JV , Rosner S , et al. Peripheral blood immune cell dynamics reflect antitumor immune responses and predict clinical response to immunotherapy. Journal for immunotherapy of cancer. 2022; 10 (6).

[22] Wei F , Azuma K , Nakahara Y , et al. Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures. Journal for immunotherapy of cancer. 2023; 11 (7).

[23] Wu Y , Zhu W , Wang J , et al. Using machine learning for mortality prediction and risk stratification in atezolizumab-treated cancer patients: integrative analysis of eight clinical trials. Cancer Med. 2023; 12 (3): 3744- 3757.

[24] Choi J , Park S , Ahn J . RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance. Sci Rep. 2020; 10 (1): 1861.

[25] Cheng S , Han Z , Dai D , et al. Multi-omics of the gut microbial ecosystem in patients with microsatellite-instability-high gastrointestinal cancer resistant to immunotherapy. Cell reports Medicine. 2024; 5 (1): 101355.

[26] Gong TT , He XH , Gao S , et al. Application of machine learning in prediction of chemotherapy resistant of ovarian cancer based on gut microbiota. J Cancer. 2021; 12 (10): 2877- 2885.

[27] Kim W , Cho YA , Kim DC , et al. Factors associated with thyroid-related adverse events in patients receiving PD-1 or PD-L1 inhibitors using machine learning models. Cancers. 2021; 13 (21).

[28] Hsu WH , Ko AT , Weng CS , et al. Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer. Journal of cachexia, sarcopenia and muscle. 2023; 14 (5): 2044- 2053.

[29] Gong L , Gong J , Sun X , et al. Identification and prediction of immune checkpoint inhibitors-related pneumonitis by machine learning. Front Immunol. 2023: 141138489.

[30] Yu X , Wu R , Ji Y , et al. Identifying patients at risk of acute kidney injury among patients receiving immune checkpoint inhibitors: a machine learning approach. Diagnostics. 2022; 12 (12).

[31] Chang WT , Liu CF , Feng YH , et al. An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline. Arch Toxicol. 2022; 96 (10): 2731- 2737.

[32] Zhang J , Cui X , Yang C , et al. A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy. Cancer Med. 2023; 12 (17): 18306- 18316.

[33] Rafiei F , Zeraati H , Abbasi K , et al. DeepTraSynergy: drug combinations using multimodal deep learning with transformers. Bioinformatics (Oxford, England). 2023; 39 (8).

[34] Torkamannia A , Omidi Y , Ferdousi R . SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy. Sci Rep. 2023; 13 (1): 6184.

[35] Yang M , Cheng H , Wang X , et al. Antithrombotics prescription and adherence among stroke survivors: a systematic review and meta-analysis. Brain and behavior. 2022; 12 (10): e2752.

[36] Murphy M , Bennett K , Hughes CM , et al. Interventions to optimise medication prescribing and adherence in older people with cancer: a systematic scoping review. Res Soc Adm Pharm. 2020; 16 (12): 1627- 1631.

[37] Yerrapragada G , Siadimas A , Babaeian A , et al. Machine learning to predict tamoxifen nonadherence among US commercially insured patients with metastatic breast cancer. JCO clinical cancer informatics. 2021: 5814- 5825.

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