From Biomarker Validation to Multi-Omics: Preparing Bioanalysis for the Next Generation of Precision Medicine
By Ruwini Rajapaksha, PhD, Postdoctoral Fellow at Lovelace Biomedical
Throughout my career, I have had the opportunity to work across academic research, startup innovation, diagnostics development, and preclinical bioanalysis. While the technologies and applications have varied— from optoelectronics research, prototype development, and breath-based diagnostics to bioanalytical assay development —one principle has remained constant: scientific innovation only creates meaningful impact when it is supported by rigorous, reproducible science.
Today, we are witnessing remarkable advances in precision medicine. Biomarker-driven diagnostics, multi-omics technologies, machine learning, and mRNA therapeutics are transforming how we understand disease and develop treatments. As exciting as these developments are, I believe their long-term success will depend on our ability to validate new approaches, maintain scientific rigor, and foster collaboration across disciplines.
Biomarkers Are Shaping the Future of Healthcare
One area that particularly excites me is the growing role of biomarkers in diagnostics and personalized medicine.
Before joining Lovelace Biomedical, I worked on developing novel diagnostic technologies, including patented breath-based platforms for COVID-19 and THC detection. These projects demonstrated the potential of non-invasive approaches to identify disease-related signatures and deliver actionable information quickly and conveniently.
Today, biomarker science continues to evolve at an incredible pace. We are moving beyond single biomarkers and toward more comprehensive diagnostic approaches that integrate multiple biological signals. This shift has the potential to improve disease detection, support earlier intervention, and help clinicians make more informed treatment decisions.
However, discovering a promising biomarker is only the beginning. Before a biomarker can support clinical or regulatory decision-making, scientists must demonstrate that it can be measured accurately, consistently, and reproducibly. That is where bioanalysis and assay validation become critically important.
Why Assay Validation Is More Important Than Ever
In my recent publication on biomarker assay validation, Regulatory and scientific considerations in PK and biomarker assay validation: lessons from bioanalytical practice,“ my co-authors and I explored best practices for developing and validating assays in alignment with evolving FDA biomarker guidance and ICH M10 recommendations for PK assays.
Biomarker assays should be validated using a fit-for-purpose approach—one that reflects the assay’s intended use rather than applying a one-size-fits-all framework. Because biomarker assays often involve endogenous analytes, complex biological matrices, and greater biological variability than traditional pharmacokinetic assays, their validation requires a tailored strategy.
Our review also highlights that reliable biomarker data depend on more than analytical performance alone. Pre-analytical factors—including specimen collection, handling, processing, and storage—can significantly influence results and, ultimately, the confidence of scientific and regulatory decisions. Addressing these variables early helps ensure that biomarker data are meaningful and reproducible.
I have seen promising projects encounter significant challenges because of issues related to specificity, accuracy, or reproducibility. In many cases, the science behind the concept is strong, but insufficient attention to analytical rigor can create obstacles later in development.
As bioanalysis evolves, whether we are working with small molecules, large molecules, immunological assays, or biomarker platforms, the same principles apply. Robust methods, reproducible results, and transparent validation strategies are essential for translating scientific discoveries into real-world applications.
Multi-Omics and Machine Learning Will Accelerate Precision Medicine
Looking ahead, one of the most transformative trends in healthcare will be the integration of multi-omics technologies with machine learning and artificial intelligence (AI).
As these approaches become increasingly sophisticated, they will also require analytical and validation strategies that evolve alongside them—a challenge our industry is only beginning to address.
Researchers can now combine information from genomics, proteomics, transcriptomics, and other biological systems to develop a more comprehensive understanding of disease. When coupled with advanced computational tools, these datasets can reveal patterns and relationships that would be difficult to identify through traditional approaches alone.
The potential impact on precision medicine is enormous. Physicians may eventually have access to integrated biological insights that support personalized diagnoses, prognoses, and treatment strategies.
At the same time, these advances raise important questions about validation and regulatory oversight. Machine learning algorithms used in healthcare must be evaluated with the same level of rigor that we expect from laboratory assays. As the field continues to evolve, regulatory frameworks will need to adapt to ensure these technologies remain reliable, transparent, and clinically meaningful.
The Expanding Promise of mRNA and oligonucleotide-based Therapeutics
Another area that could significantly influence the future of medicine is mRNA and oligonucleotide-based therapeutics. Unlike conventional small-molecule drugs and biologics, which primarily target proteins, these nucleic acid-based therapies can directly modulate gene expression, enabling the treatment of diseases that were previously considered undruggable. By leveraging the body’s own cellular machinery, mRNA therapeutics can instruct cells to produce therapeutic proteins, while oligonucleotides including antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), microRNA modulators, and splice-switching oligonucleotides can selectively regulate RNA function and gene expression.
The success of mRNA vaccines during the COVID-19 pandemic demonstrated the power and flexibility of this platform. Today, researchers are exploring applications that extend well beyond infectious diseases, including rare diseases, chronic conditions, and personalized therapies. Similarly, oligonucleotide therapeutics have achieved significant clinical milestones, with several approved products successfully treating genetic, metabolic, neurological, and cardiovascular disorders through precise modulation of disease-causing genes.
A key advantage of these therapeutic platforms lies in their capability and versatility. Once the delivery system and manufacturing processes are established, the therapeutic sequence can be readily modified to target different genes, accelerating drug discovery and development. Advances in lipid nanoparticle delivery systems are creating opportunities to develop therapies that can target multiple disease pathways simultaneously. Advances in chemical modifications, lipid nanoparticles, and targeted delivery technologies have further improved the stability, potency, and tissue-specific delivery of these molecules, expanding their therapeutic reach across a broad spectrum of diseases.
As these therapies become more complex, the need for robust analytical methods will only increase. Ensuring the quality, stability, and performance of these products will require the same scientific rigor that underpins every successful bioanalytical program.
Looking Forward
The next decade will bring tremendous advances in biomarker science, multi-omics platforms, machine learning, mRNA and oligonucleotide-based therapeutics. These technologies have the potential to fundamentally change how we diagnose and treat disease.
Yet regardless of how sophisticated our tools become, the principles that drive scientific success will remain unchanged. Scientific rigor, reproducibility, and fit-for-purpose validation form the foundation that allows innovation to translate into meaningful advances for patients. Combined with collaboration across scientific disciplines, these principles will ensure that the next generation of precision medicine is built on evidence that is both innovative and trustworthy.
As our industry continues to push the boundaries of what is possible, I am excited to see how these emerging technologies improve patient care and accelerate the future of precision medicine.
About the Author
Ruwini Rajapaksha, PhD is a Associate Research Scientist at Lovelace Biomedical, where she works across multiple preclinical bioanalytical disciplines, including small-molecule LC-MS/MS, large-molecule assays, PCR, immunology, and aerosol studies. Her scientific career spans analytical chemistry, diagnostics development, startup innovation, and translational research. Prior to joining Lovelace, she contributed to the development of patented breath-based diagnostic technologies for COVID-19 and THC detection and worked on federally supported diagnostic and environmental monitoring initiatives.
Dr. Rajapaksha is passionate about bioanalytical assay development, bioanalytical method validation, precision medicine, and emerging therapeutic technologies, including mRNA and oligonucleotides-based platforms. Her most recent publication, “Regulatory and scientific considerations in PK and biomarker assay validation: lessons from bioanalytical practice“ (2026), provides practical guidance for developing regulatory-compliant biomarker assays aligned with evolving FDA and ICH expectations, helping advance the scientific rigor necessary for the next generation of precision medicine.