Personalized Medicines a Milestone Therapeutic Journey

Personalized medicine (PM), also known as precision medicine (a milestone therapeutic Journey), acts as the “one for all’ benefit medicines derived from the specific genes of the patient undergoing the therapy. President Obama announced the Precision Medicine Initiative (PMI) in January 2015. These PMs collect data under the Human Genome Project, launched by NIH and the Department of Energy, while being established by HHS in 1989. PMs are used for different genetically occurring diseases and acquired diseases like Cancer, Alzheimer’s, and Diabetes.

These PMs are currently produced as tablets in oral form. However, other forms are being developed for the pediatric and geriatric dosage. For the development of personalized gene sequencing, the adenine, guanine, cytosine, and thymine are taken and altered in the Nex-gen sequencing via the CRISPER technology. This technology has formed the base for the CRISPER-Cas9, a gene editing technology where the DNA would be targeted, and the genetic code would be altered.

FDA Guidance:

USFDA has given brief guidance for the Next-generation sequencing-based tests for the WES (Whole exome Human data sequencing) and the targeted human DNA sequencing. If one suspects that the patients have any germline diseases, they can use this approach to develop, validate, research, and uptake clinical trials with the guideline. This guideline is called, “Considerations for Design, Development and Analytical validation of Next Generation Sequencing(NGS)-based In-Vitro Diagnostics and Intended to Aid in the Diagnosis of Suspected Germline Disease” and “Use of Public Human Genetic Variant Databases to Support Clinical Validity for Genetic and Genomic-Based In Vitro Diagnostics.” These guidances talk about the data collection methods, Storage methods, how a test for these products can be tested, specimen collection, analytics, and test performance. NIH has a follow-up on the Bayh-Dole Act to patent and license intellectual property.

Over the past two decades, the study of the human genome and its variations has advanced alongside innovations in laboratory techniques. Automated DNA sequencing and PCR, including automated thermal cyclers, have greatly expedited genome sequencing. Techniques like microsatellite DNA and single-stranded conformational polymorphisms (SSCPs) have emerged for studying sequence variations.

Overview and History:

Exploring the following topics has enriched genomic research:

• Expressed sequence tags (ESTs)
• cDNAs
• Antisense molecules
• Small interfering RNAs (siRNAs)
• Full-length genes
• Haplotypes

The latest focus is on single nucleotide polymorphisms (SNPs), which is pivotal in large-scale pharmacogenomic studies. They are the most abundant form of variation in the human genome (occurring at a frequency of 1% or more). Haplotype maps, charting blocks of closely linked SNPs, extend the SNP exploration. High-throughput genotyping relies on automated oligonucleotide chip technology, replacing traditional gel methods. Microarray technology aids gene-expression profiling, offering insights into phenotypic associations with diseases and drug responses. Progress in these techniques is underpinned by advances in nucleic acid isolation, amplification, hybridization, detection technologies, and sophisticated bioinformatics software for analyzing vast volumes of genetic data.

FDA believes that the clinical trial validity depends on the data and the belief of the procedure on the genetic variant database where the genetic defaults and the mutation errors can be discovered and rectified, and the data is being obtained based on genetic and genomic-based tests of the patients.

FDA Approval Process:

For the approval process, the FDA asks the sponsor to submit a carefully drafted documents such as the following:

• General operations of the genetic variant database.
• Personally identifiable information and protected health information confidentiality and Privacy.
• Data integrity and security.
• Curation, variant evaluation, and re-evaluation.
• Training for curation, evaluation, privacy and security, and other relevant activities.
• Validation studies and SOPs.
• Documentation of the qualifications of the individuals evaluating variants and policies for approving those individuals.
• Data preservation plans.
• Conflict of interest on policies and disclosures of conflicts of interest.
• A commitment to make all the recommended data public.
• A cover letter

The Specifications of the cover letter for FDA:

• Statement of the types of variants the genetic variant database assertions address (e.g., germline).
• Scope or portion of the database for which recognition is being sought.
• Point of contact, Entity name.
• Statement that the submitters believe, to the best of his or their knowledge, that all information they have submitted is truthful and accurate and that they have not omitted any material facts.

Testing Process for Personalized Medicine:

The testing of these PMs consists of very intriguing processes, which are four different kinds:

N-of-1 Clinical trials:

Equipoise arises when there is no reason to believe that any intervention in a set better matches an individual’s profile. In such cases, N-of-1; or single-subject trials become valuable, focusing on an individual’s response to different interventions to determine the optimal one. These trials often use crossover designs, such as “ABABAB,” where “A” and “B” denote different interventions. Incorporating randomization, blinding, Washout periods, multiple endpoints, and N-of-1 trials allows intervention comparisons. While crossover-based N-of-1 trials may be impractical or unethical in acute or life-threatening conditions, sequential designs continuously monitor measures in real time to assess intervention effects. N-of-1 trials emphasizing personalized optimal intervention overpopulation averages are particularly relevant when equipoise is present in clinical practice.

Intervention Matching Trials:

When evidence suggests that individual patient profiles can guide effective interventions, the challenge is to test whether matching interventions based on these profiles yield better outcomes than alternative strategies. Testing each match may pose logistical and financial difficulties. This could lead to the emergence of “basket” and “umbrella” trials, predominantly in oncology. Basket trials enroll patients regardless of cancer type, while umbrella trials focus on a specific tissue. Tumor profiling, often through DNA sequencing, identifies actionable driver perturbations. Understanding the mechanisms of action allows matching interventions to tumor perturbations (e.g., using cetuximab for tumors with mutated and overexpressed EGFR genes). Eventually, we will direct each patient to a specific intervention basket (e.g., the EGFR inhibitor basket). These trials aim to evaluate whether outcomes improve when interventions are based on matching schemes, compared to schemes without profiling and matching for individual patients.

Adaptive clinical trials:

Adaptive and sequential clinical trials, traditionally employed for decades, have gained recent prominence in the context of PM. These trials aim to minimize patients’ time on potentially inferior therapies. In PM scenarios with equipoise among interventions or when evaluating untested versus conventional treatments for an individual, conducting extensive N-of-1 studies may be impractical or harmful. Instead, adaptive trials collect real-time biomarkers reflecting responses or adverse effects from individual participants. This allows for a continuous monitoring. Therefore, the patients can switch to a new intervention if signs suggest an ineffective intervention. Despite real-time evaluation and complex data analysis challenges, adaptive designs are ethically sound. Overall, integrating adaptive components into N-of-1 trials, aggregated N-of-1 trials, and intervention-matching trials is generally feasible. As such, Murphy and colleagues’ work is noteworthy for its emphasis on minimizing the duration of patients’ exposure to inferior treatments in adaptive trials.

Conclusion (A Glimpse into the Future): 

As time passes, we will use PM in every part of the treatment, with the treatment for the patients as “One for All” therapy.  The safe, responsible, and optimal use of research results required for Precision Medicine (PM) is becoming a routine in clinical settings. Hence, clinical decisions rely on multidisciplinary teams integrating novel health-related professions. The education of healthcare professionals has embraced the interdisciplinary aspects of PM, while clinicians and researchers work closely to ensure the rapid development and implementation of PM solutions. Equitable access to PM services has become a reality for all citizens, ensuring optimized healthcare interventions for effectiveness and fairness.

Societal values consistently guide resource allocation within healthcare systems. A secure and streamlined flow of health data from citizens and healthcare systems to regulatory authorities and research entities is in place. This could enable healthcare providers and researchers to leverage personal data from electronic health records (EHRs) efficiently for PM applications. Harmonized solutions guarantee data privacy, safety, and security in health data management. Treatment and prevention strategies, tailored based on personal data, not only enhance the well-being of citizens but also reduce costs and risks. The PM landscape maintains a reasonable equilibrium between investment, profit, and shared benefits for citizens, supported by fitting business concepts and models.

References:

1. Astrid M. Vicente, Wolfgang Ballensiefen, amp Jan-Ingvar Jönsson, How personalized medicine will transform healthcare by 2030: the ICPerMed vision, 18 JOURNAL OF TRANSLATIONAL MEDICINE (2020), https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02316-w.
2. Center for Devices and Radiological Health. (2018, September 27). Use of Public Human Genetic Variant Databases to Support Clinical Validity for Genetic and Genomic-Based In Vitro Diagnostics. US Food And Drug Administration. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-public-human-genetic-variant-databases-support-clinical-validity-genetic-and-genomic-based-vitro.
3. FDA, Precision Medicine regulations, FDA (2019), https://www.fda.gov/medical-devices/in-vitro-diagnostics/precision-medicine.
4. Lori Knowles, Westerly Luth, and Tania Bubela, Paving the road to personalized medicine: recommendations on regulatory, intellectual property, and reimbursement challenges, 4 JOURNAL OF LAW AND THE BIOSCIENCES 453 (2017), https://academic.oup.com/jlb/article/4/3/453/4584308.
5. National Human Genome Research Institute, Personalized Medicine, GENOME.GOV (2022), https://www.genome.gov/genetics-glossary/Personalized-Medicine.
6. Public health European Commission, Personalized medicine, HEALTH.EC.EUROPA.EU, https://health.ec.europa.eu/medicinal-products/personalised-medicine_en#:~:text=Personalised%20medicine%20is%20a%20medical.

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