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Global Translational Medicine Game-changing drug response prediction

the development of precision medicine, which leverages drug response prediction and drug repurposing process to
patients’ genomic information to provide targeted ultimately reach every patient without creating too much
diagnostics and personalized therapeutics. This innovative of a burden on the healthcare system.
approach aims to offer more effective treatments by focusing
on the specific genetic makeup of each individual’s cancer. 2. Precision drug development: Too few and
too long
New precision drugs based on the exact DNA mutations
that drive the cancer are needed to help the millions of Over a century of relentless research, and yet, cancer
patients diagnosed with some form of cancer each year. still ranks among the top causes of death. Recent
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Where available, targeted therapy or immunotherapy breakthroughs in treatment are promising, but there is
has been the standard of care to treat cancer; however, a long way to go for these scientific advancements to
only 20–30% of cancer patients will be qualified and this translate into meaningful clinical outcomes. Astonishingly,
practice has left out an approximately 70–80% of patients only about 3% of oncology drugs ever reach the market.
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due to their ineligibility (or negative biomarker testing), Even then, many offer minimal improvements to patient
3,4
making precision oncology incomplete and inefficient. lifespan or quality of life. The pharmaceutical industry’s
Most significantly, two persons might have the same type ultimate mission is to deliver safe and effective drugs, but
of cancer, but their diseases can behave differently and the escalating costs and high failure rates pose significant
respond differently to the same treatment. That is why challenges. Developing a drug can cost up to $2.6 billion
we desperately need a precision and personalized tool for and can take up to 15 years, with a staggering 90% of drugs
drug response prediction, beyond traditional biomarker failing during clinical trials. 4
tests or companion diagnostics, and beyond the responder These failures can generally be split into three
population. categories: (i) incomplete understanding of underlying
We should no longer focus on a 60° angle between biology: genomic and transcriptomic data often fail to
targeted therapy and the patient responders dictated by the reveal cancer’s full complexity, leading to partial insights
guidelines; instead, we should constantly look around with into tumor biology; and (ii) incorrect drug targets: some
a 360° spectrum for not only actionable DNA alterations cancer drugs act on unintended targets. Clinical trial
but also drug response prediction technologies for non- analyses show cases where off-target effects lead to efficacy
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responders. The idea would be the patient comes in and but also to toxicity and side effects ; (iii) lack of effective
then gets an answer about alternative treatment options if biomarkers: this limits the ability to identify and stratify
not qualified for targeted therapy or immunotherapy, and patients, making it difficult to monitor treatment responses
we could get patients into the system immediately so that in clinical settings.
they could get the right lifesaving medicines. The broader The gap between animal and human translation,
the treatment options, the more it will benefit patients. alongside ethical efforts to reduce animal testing, has driven
A new data network that integrates cutting-edge the industry toward earlier testing with patient-relevant
research on the molecular makeup of diseases with cell-based models such as tumor xenografts, primary
clinical data on individual patients could revolutionize the tumor cells, and cancer cell lines. Progress is further
classification of diseases, ultimately enhancing diagnosis bolstered by advanced and more physiologically relevant
and treatment. The resulting “precision medicine” would 3D organoid and spheroid cultures. Highly sensitive
define diseases based on their underlying molecular causes analytical techniques have greatly enhanced the predictive
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and other factors, in addition to their traditional physical accuracy of these models. However, despite initiatives to
signs and symptoms. The ability to exploit the actionable mitigate risks in drug development, relying on the same
information gathered from tumor genomics to provide tools and methods will not likely yield innovative, first- or
reliable and verifiable drug response prediction changed best-in-class targets or transformative advancements.
the dynamic of molecular diagnostics, as well as precision 3. Precision drugs are only for responders
therapeutics. This review highlights the rapidly evolving
landscape, exciting new developments and key challenges Oncology became the leading therapy area globally in 2010
in drug response prediction with a particular focus on and since then has spectacularly increased its value over
the most recent systems in predicting drug efficacy in the past decade. In 2020, it is still the leading therapy area
various settings. With the advent of state-of-the-art by size and growth. Innovation has fueled this phenomenal
technologies such as liquid biopsy, coupled with genomic growth with the sustained launch of diverse medicines,
and transcriptomic profiling and machine learning/ most of which are targeted with various mechanisms of
modeling, we now have the opportunity to transform the action. As we have developed a deeper understanding of

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