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Global Translational Medicine Game-changing drug response prediction

Figure 1. The key drivers for drug response prediction market. The increasing prevalence of cancer and the pressing need for early diagnosis and
intervention are fueling the demand for drug response prediction technologies. Accurate drug response prediction can help identify personalized gene
signatures linked to effective drugs, enabling early intervention and better patient outcomes.

Figure 2. Current challenges associated with drug response prediction methodologies

expertise, large gaps when translating findings to real- discovery, and model-based data integration and artificial
world patients, and the production of inaccurate results; intelligence (AI). Therefore, it is not far-fetched to imagine
(iii) clonal heterogeneity also poses a problem, as precision that the advancement of machine learning is having a
medicine in oncology relies on accurately characterizing significant impact on precision medicine. Drug response
the molecular profile of individual tumors at the start of prediction based on the genomic or transcriptomic profile
treatment. However, tumor profiles are dynamic, and of a cancer patient is one of the hallmarks of precision
targeting the dominant clone can lead to the emergence of oncology. Despite improvements in in silico deep learning
resistant subclones; (iv) limited biomarker identification is approaches for drug response prediction, there is still an
another hurdle, as precision medicine depends on reliable urgent need to upgrade from “one-size-fits-all” digital
cancer biomarkers to track disease progression. The models to technologies that could truly offer high accuracy
collection of high-quality genomic data for these biomarkers as well as interpretable predictions for real-world real-life
is challenging, resulting in a shortage of clinically validated applications, especially in the non-responder population.
biomarkers for predicting and monitoring outcomes in Recent advancements in deep learning have played a
patients receiving targeted drugs; (v) personalization adds crucial role in aiding scientists to develop drug response
further complexity, as regulatory compliance demands prediction models. These machine training computation
stringent safety and efficacy standards for targeted drugs, techniques contribution to this field is significant, but they
which require rigorous clinical trials involving diverse all missing vital clinical validation, a key step to translate
patient populations with specific cancer-associated
genetic mutations. These standards are often difficult to pre-clinical findings to clinical utility (Table 1).
meet, posing a significant barrier to the development and The field of machine learning, particularly deep neural
implementation of precision medicines. Most significantly, networks (DNNs), has been propelled by the surge in big
all in silico drug response prediction algorithms apply one- data, enhanced computing power, and cloud storage across
size-fits-all approaches (i.e., population-wide approach). various sectors, including both industrial and academic. 11-17
In medicine, DNNs positively impact three key areas:
6. In silico drug response prediction models enabling clinicians to interpret data rapidly and accurately,

Translational precision medicine consists of key areas such enhancing connectivity and reducing medical errors within
as multi-omics profiling of patients, digital biomarker health systems, and empowering patients to process their

Volume 4 Issue 2 (2025) 6 doi: 10.36922/gtm.5091