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Global Translational Medicine Game-changing drug response prediction

PGA is designed to improve therapeutic efficacy among and immunotherapy together, making it difficult to
the increasingly prevalent patient non-responders. The key accurately mirror real-world conditions for testing the
to its effectiveness lies in the genetic signature of the patient, efficacy of treatment plans. The first-ever patient-tailored
which is meticulously generated to maximize the predictive PGA approach can work with the complexity of more
power of drug efficacy in real time, thereby increasing the complicated combination therapy, with the hope to
chances of successful therapeutic intervention. Unlike improve the response rates of patients that are currently
traditional biomarker testing for DNA mutation detection, left out of targeted therapy due to the lack of actionable
which only reflects tumor itself, the mRNA expression mutations or become unresponsive to current treatments.
signature is designed to represent both tumor and non- Drug response prediction has undeniably transformed
tumor microenvironment, maximizing the accuracy of precision oncology, enabling tailored cancer care that
drug efficacy prediction even without a biomarker test. 11 aligns with each patient’s unique genetic profile. This

Having the PGA gene-to-drug technology is essential advancement has paved the way for more effective and
to treatment selection and decision-making as an extended personalized treatment strategies, ultimately improving
option to patients who are not responders and had patient outcomes. The growing interest and demand of
exhausted other treatment options. Necessity for such a more effective and durable therapies have enabled quick
technology could mean that the progressive disease is in transition of drug response prediction tests from the bench
place and that previous efforts to destroy the tumor have to bedside. Despite advancements in precision therapies,
been unsuccessful. In the end, if none of that works, we many patients remain unresponsive due to the complex
have PGA test which will actually allow clinicians to direct nature of the human genome. Scientists are still in the
the treatment strategy to other tumor targets and pathways. early stages of decoding genetic data and translating it
into actionable information. Therefore, there is a critical
8. Conclusion and future perspectives need for diagnostic technology that can leverage a

The management of cancer has improved in recent years, patient’s unique genetic data to make accurate therapeutic
with targeted therapy and immunotherapy gradually predictions for each individual non-responder.
complementing chemotherapy and radiotherapy. Despite The breakthrough PGA technology no longer focuses
the promise of these precision treatments for treating on a 60-degree angle between targeted therapy and the
cancer, but a non-responsiveness case would catapult patient responders dictated by the guidelines; instead,
clinicians back into the cycle, figuring out other therapeutic it constantly looks around with a 360° spectrum for
alternatives. Approximately 70 – 80% of cancer patients identifying not only disease-relevant DNA sequences but
will be disqualified for or fail to respond to precision also strategies to integrate liquid biopsy, gene expression,
medicine, and designing a predictive tool of drug response and drug response prediction, for use in the non-responder
remains a significant unmet need in this population. Other patients. The future will tell, but the low patient eligibility
than doing imaging, performing a physical examination, and poor response rates of precision medicine could serve
and clinically monitoring the patient, there is no effective for clinicians as a wake-up call for having alternative and
companion tool to gauge drug efficacy in a particular reliable drug response prediction tools. Indeed, it was a
patient. lack of both drug efficacy and patient benefit the medical
Studies have proposed neural network deep learning, community experienced that prompted the invention
machine learning or AI training models as potential of the PGA technology. Although biomarker tests offer
predictors of drug response. However, none of these unprecedented capabilities, they ultimately qualify
in silico attempts have been clinically tested. There is a huge the minority of patients for precision medicine, while
realistic gap between the availability and the applicability excluding the majority. Precision medicine can no longer
of these models. On the contrary, the transformative PGA solely count on superiority in the field of biomarker testing,
test takes plasma samples from patients, extracts and and we should not solely focus on precision therapy, as any
profiles cell-free mRNA, obtains patient-derived gene other important, viable treatment options available are
expression signatures, which are then used to screen, worth explored too. The broader the treatment options, the
match, and catalog potentially effective drugs. Results more it will benefit patients.
from in vitro tests are combined with drug databases to Although precision medicine appears to be promising
power in silico predictive models, pinpointing the drugs to in future, the success will be primarily determined by the
which a specific patient will have good responses. utilization of drug response prediction tools to capitalize
Cancer treatments are complex, especially when on the growth trends and meet demands from the non-
they are combined, like using chemotherapeutic drugs responder population.

Volume 4 Issue 2 (2025) 9 doi: 10.36922/gtm.5091