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Applications of Bioanalytical Services in Personalized Medicine and Precision Healthcare

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Today medicines are becoming more and more personal. All people are not the same. Each individual has a distinctive biomolecular impression. Personalized medicine and precision healthcare focus on these unique differences to tailor therapeutic approaches for individual patients. With the advent of more sophisticated technologies, personalized medicine has now become a reality.

Personalized medicine often relates to pharmacogenomics. In this approach, scientists try to correlate therapeutic effects or adverse reactions with specific genomic biomarkers. These biomarkers could be gene expression profiles or genotypes. Today the FDA has approved several drugs that have pharmacogenomic information.

Thus, many pharmaceutical companies are taking advantage of this growing field of precision medicine. Some companies have in-house teams to monitor pharmacogenomics studies,while many opt for bioanalytical services CROs to conduct their pharmacodynamic projects. Hence, bioanalytical labs have several applications in personalized medicine. The current article discusses the applications of bioanalytical services in personalized medicine and precision healthcare.

Pharmacogenomics Studies

FDA defines pharmacogenomics as studying the features of DNA/RNA with respect to drug exposure. This requirement translates to conducting bioanalytical assays through genotyping, gene expression studies, and qPCR. Drug development companies use this bioanalytical testing data in numerous ways, including toxicity and PK evaluations, developing and validating biomarkers, and decision-making about drug dosage and treatment.

Thus, clinical bioanalysis services have numerous applications in pharmacogenomics evaluation. One may begin with gene expression analysis and end up with validating a novel biomarker, while the next day may involve qPCR assays and completing regulatory paperwork. 

Automation is the Key to a Robust Bioanalytical Testing

Bioanalytical assays are all about generating high-quality data. Contamination is one of the primary concerns affecting assay results, for example, contamination while performing qPCR with human DNA. Bioanalytical labs employ automation to minimize contamination as much as possible. They use automated technologies such as automated liquid handlers to offset risks associated with human error.

Automation also provides consistent assay handling. Consistent handling is crucial for quantitative analysis. Except for genotyping, most bioanalytical studies, such as bioavailability, dose-response, and pharmacokinetic studies, rely on quantifying absolute measurements. Any inconsistencies in assay handling will negatively impact assay measurements. Besides providing accurate performance, bioanalytical labs ensure that their automated systems comply with regulatory requirements for performance qualifications.

Providing More Support for Complex Biomolecules 

Personalized medicines strive on pharmacogenomics data. Although pharmacogenomics data help target specific compounds to the right patient, FDA submission of pharmacogenomics data is voluntary for conventional small molecules. However, this requirement changes with newer therapeutics, such as gene therapy and nucleic acid-based drug products. Here, pharmacogenomic data is crucial to understand the safety and toxicity profiles. Hence, bioanalytical services can help provide robust regulatory data for FDA submissions.

Also Read: Key Considerations and Challenges in Each Phase of Drug Development

Biomarker Strategy and Real-world Evidence

Bioanalytical labs specialize in biomarker strategy and development. They can guide pharmaceutical companies in evaluating and choosing the most appropriate technologies and methods for biomarker analysis. They also deliver reliable data that aligns with regulatory submissions. Moreover, several bioanalytical labs are focused on providing real-world data. They offer a multidisciplinary approach to yield insights that are fit for purpose and not just raw data.

Read more about Artificial Intelligence and Machine Learning.

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