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Global Translational Medicine Game-changing drug response prediction

the underlying biology and natural history of oncology, prediction is another core element of precision medicine.
more sophisticated therapies have emerged with a clear Drug response prediction is about using genomic and
shift toward precision medicine. digital methods to forecast how patients will react to certain
Biomarker testing involves examining genes, proteins, medicines. It involves looking at various types of data
and other targets-of-interest (known as biomarkers or sources, such as genomics or transcriptomics, drug classes,
tumor markers) to gather information about cancer. Each and preclinical and clinical datasets, to predict how well a
individual’s cancer presents a unique pattern of biomarkers, patient will respond to a particular drug. The integration
some of which can influence the effectiveness of specific of precision medicine in cancer treatment is on the rise,
cancer treatments. Coupled with the rise of precision driven by multiple factors. This shift significantly enhances
medicine, oncology has also seen biomarker tests not just the potential for accurate drug response prediction in the
entering the mainstream but also shifting the way decisions years to come (Figure 1).
are made. Undoubtedly, the rise in genomic information is The increasing global cancer incidence is a key factor
also a driver of this paradigm shift. The use of biomarkers driving the development and treatment prediction of
is at the core of the precision medicine movement to precision medicines for oncological disorders. According
provide the right therapy to the right patient population. to the American Cancer Society, approximately 2 million
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As valuable as it is to use genetic information to prescribe new cases are expected to be diagnosed in the United
the right drugs to the right patients, there are multiple States in 2023. This highlights a critical need for more
challenges that come with it. New targeted therapies with effective and targeted treatments to address the global
biomarkers do not always fit in every cancer patient, burden of cancer. Personalized healthcare, which tailors
they can make the treatment landscape more complex, treatment to an individual’s unique genetic makeup, has
and cause patient coverage to shrink and fragment, thus significantly improved the quality of life for cancer patients.
overwhelming healthcare professionals. The favorable outcomes of precision oncology have led to
Biomarkers have become an essential part of the a higher demand for such customized drugs and spurred
treatment paradigm and the decision-making process for the development of drug response prediction pipelines,
many tumor types. In a global healthcare environment such as biomarker testing. Companion diagnostics play a
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where we see widespread cost containment measures, it is key role in identifying genetic mutations in cancer patients
important for payers and other key stakeholders to identify and determining those most likely to respond to precision
patient subgroups who will benefit from certain drugs. It medicines being evaluated in clinical trials. These tests
is certainly invaluable for the patients receiving cancer not only increase the success rate of clinical trials but also
therapies to have this reassurance that the therapy they streamline regulatory approval for these drugs, creating
are receiving is targeting the specific mutation underlying lucrative opportunities for the growth of precision
their illness. oncology and drug response prediction markets.
The overall success of this segment does not mean
that all precision therapies with biomarkers are set to 5. The challenges of drug response
thrive. Typical challenges to overcome include benefiting prediction
small patient subgroups (as responders), low response Over the recent years, scientific and clinical communities
rates, evolving resistance, disease relapse, and therapy have developed various drug response prediction strategies,
exhaustion. in addition to biomarker testing, often consisting of in silico
4. Emerging drug response prediction data mining, pooling, modeling, regression, classification,
and training with digital algorithm computation
trends (Figure 2). There are still key limitations that need to be

The discovery of precision drugs and the customization addressed: (i) low patient coverage: Targeted therapy
of cancer therapy remain a daunting task. The hallmark or immunotherapy has saved countless lives but only
of precision medicine is to tailor more effective diagnostic 20 – 30% of patients are eligible for the treatment. Further,
and anti-cancer therapy to each individual patient. the average response rates to these precision therapies are
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Although biomarker tests (or companion diagnostics) not high, again around 20 – 30%. Predicting which drugs
offer unprecedented capabilities, they ultimately qualify would benefit those non-responders could significantly
the minority of patients for precision medicine, while help this desperate population; (ii) inadequate technologies:
excluding the majority. Precision medicine can no longer despite the promising potential of deep machine learning
solely count on superiority in the field of biomarker in evaluating drug response through genomic data, it faces
testing. As a gap-filler for biomarker testing, drug response significant challenges. These include a lack of technical

Volume 4 Issue 2 (2025) 5 doi: 10.36922/gtm.5091