Month: September 2022

With the help of precision medicine, or personalised medicine, modern medicine has moved away from a ‘one size fits all’ approach to treating disease and towards therapeutic approaches that are tailored to individuals and subgroups. These treatments are designed to be more efficacious due to targeting population subgroups based on their genetic or molecular nuances, rather than operating on the assumption that all bodies function and respond the same way and to the same degree to a given treatment. Molecular knowledge can now be utilised to tailor treatment to the patient at the correct dosage and time point, usually with the aid of pharmacogenomic approaches and molecular biomarkers.

Information about an individual’s genetic makeup, such as genetic variants that may influence treatment efficacy, toxicity, and adverse events can help to determine how patients will respond to a certain treatment. In addition to genomic, recent technological advances have led to the identification of many transcriptomic and proteomic biomarkers. This knowledge is useful in all stages of therapeutic development and can influence both the design of the therapeutic itself and of the clinical trial.

Drug Development

Inter-individual variations in drug response can result from polymorphisms in drug metabolizing enzymes. Thorough examination of gene expression and mutations in disease populations can lead to the identification of distinct disease subpopulations that share certain characteristics.  Further exploration of these genes and their interactions can uncover possible drug target genes for the treatment of a disease subpopulation.

Furthermore, an awareness of genetic variation in disease subpopulations means that the involved pathways and components can be more accurately recreated in pre-clinical studies. Bringing the gap between animal and human toxicity findings allows for more representative disease models. This allows variability in treatment response and optimal dosage to be explored more precisely.

Clinical Trial Design

Originally, clinical trials were designed to evaluate differences between novel treatments and standard treatments or controls, not among individual responses in treated groups. As a result, a therapeutic that was very effective in only a subgroup of the trial population may not have produced significant results and a therapeutic that caused adverse events in only a small subgroup could have been deemed too toxic for overall use.

The goal of clinical trials to gain regulatory approval remains unchanged. With the emergence of precision medicine come biomarker-driven trials that include patient subgroups in their design. Master protocols for trials enable the application of one treatment to multiple diseases, or multiple treatments to one disease, allowing a trial to adapt during its course. This room for adaptation can reduce financial impact due to ineffective treatments being abandoned earlier and targeting the most suitable groups. Incorporating a diagnostic assay in trial design can offer multiple advantages and prevent research from straying in the wrong direction.

Targeted therapies can be tested in the most appropriate patient groups likely to benefit by biomarker testing of patients prior to clinical trial participation. Screening patients for those more likely to respond well to treatment gives a greater estimate of treatment effect in the subgroup.  This increases the likelihood of demonstrating efficacy in a clinical trial. It also reduces the size of the sample population required to see statistically significant results, which can speed up the process.

Identifying responders before enrolment in such a manner minimises the number of exposed patients who would not benefit from treatment. Decreasing the risk of exposing non-responders to potential adverse events can improve the benefit/risk analysis.

Patient stratification is another aspect of trial design that utilises patient’s molecular biomarker profiles. Stratifying trial participants into subgroups can classify disease subtypes. Particularly in oncology, genomic approaches can guide the stratification of patients by their tumour mutations. It is notably useful in umbrella, basket, and platform trials and can reduce the financial impact by allowing for adaptive trials.

Umbrella trials test multiple targeted therapeutics in different biomarker cohorts of a single disease. Basket trials, on the other hand, test one or more targeted therapeutics in a patient cohort with matched biomarkers. Platform trials have a randomised structure and allow the evaluation of multiple targeted therapeutics in multiple biomarker-selected populations.

Application in developed therapeutics

While precision medicine approaches are most beneficial when included throughout the drug development process, their application can also improve or salvage existing treatments and prevent a clinical trial from failing. For example, a developed drug may cause severe adverse events in a small disease subpopulation.  Upon investigation it is found that the drug has a secondary target, which is only present in that subgroup.  With this knowledge, patients can be screened for presence/absence of the safety biomarker and intervention with said drug can be avoided in that subgroup while continued in the remaining population.

Alternatively, a drug may have clinically meaningful results in only a small number of patients. The responsive subgroup can be explored for potential biomarkers associated with degree of responsiveness to treatment. The clinical trial can then resume with a focus on patients likely to respond well to the therapeutic.

Response Monitoring

Throughout and after a clinical trial, biomarkers can be used as a means of observing patient response to intervention, and account for variability in response. Safety and efficacy monitoring markers will reveal individual cases where treatment is working effectively or needs to be halted due to adverse events. For example, a cancer-related gene mutation or protein detected in blood may no longer be present after successful treatment has been administered, showing that the treatment has worked.

Identified responders or non-responders can be further stratified into subgroups and studied.  Genomic information can aid in the understanding of outliers and changes to treatment response. This will contribute to disease and therapeutic understanding, so that the right patients can be given the right dose, getting the most benefit out of treatment.

Challenges of Precision Medicine

It should be noted that the development of a targeted therapy requires the right data, both for the identification of the drug target and suitable patients. Molecular data from disease populations in previous studies may not always be available during drug development. If available, it may not be the correct type of data or generated by the most appropriate assay. Developing a targeted therapy is not possible without suitable data to understand disease mechanisms and identify putative drug targets.

Biomarker-driven therapies require genetic tests and companion diagnostics to identify and distinguish suitable patients. Incorporating diagnostic methods in a clinical trial is an added cost and the process can be burdensome as it can make participant recruitment harder. Clinical intervention according to the results of stratification should also be well-defined before a trial phase commences.
 

References
Di Liello, R., Piccirillo, M., Arenare, L., Gargiulo, P., Schettino, C., Gravina, A. and Perrone, F., 2021. Master Protocols for Precision Medicine in Oncology: Overcoming Methodology of Randomized Clinical Trials. Life, 11(11), p.1253.
Dugger, S., Platt, A. and Goldstein, D., 2017. Drug development in the era of precision medicine. Nature Reviews Drug Discovery, 17(3), pp.183-196.
Mirsadeghi, S. and Larijani, B., 2017. Personalized Medicine: Pharmacogenomics and Drug Development. Acta Med Iran, 55(3), pp.150-165.
Woodcock, J., 2007. The Prospects for “Personalized Medicine” in Drug Development and Drug Therapy. Clinical Pharmacology & Therapeutics, 81(2), pp.164-169.