The role of bioanalysis as the foundation of precision medicine

Precision medicine, also known as personalised medicine, is transforming healthcare by providing tailored treatments to patients suffering from life-threatening conditions. We are all unique, and our bodies respond differently to sickness, external factors, and therapeutic intervention. Precision medicine embraces this fact and diverges from the one-size-fits-all approach that has dominated medicine for so long. Personalising treatments for the individual means drug developers can boost efficacy, reduce side effects, and improve patient outcomes.

Bioanalysis is integral to this medical approach, driving research forward from drug discovery to clinical application. Bioanalytical techniques underpin the development of personalised medicine in three critical ways:

• Identifying and verifying biomarkers for diagnosis and prognosis;
• Monitoring therapeutic responses, allowing for timely interventions in treatments;
• Developing safer, more effective drugs tailored to specific patient groups.

What follows is a closer look at these critical bioanalytical processes and their transformative roles in turning the vision of personalised medicine into a practical reality.

Identifying and validating biomarkers for precision medicine

Biomarkers are found in blood, tissues, and even the air we exhale. They can be as simple as a patient’s heart rate or much more complex, like cytokine levels indicating immune responses. These molecular signatures are vital to the advancement of personalised medicine, enabling precise targeting of specific diseases.

Human Epidermal Growth Factor Receptor 2 (HER2) is one of the most well-known examples of a biomarker used in precision medicine. When overexpressed, HER2 facilitates aggressive growth in breast cancer cells. Identifying and validating this biomarker helped develop drugs like Herceptin, which targeted HER2-positive breast cancers and drastically improved treatment for that subset of patients. This form of cancer is aggressive and was previously associated with poor outcomes. However, with the introduction of HER2-targeting agents, survival rates rose to 90% in HER2-postive breast cancer, which is diagnosed early and treated with chemotherapy and dual antibody therapy.

To achieve similarly effective breakthroughs, researchers use a mixture of innovative and established bioanalytical methods to identify new biomarkers. Mass spectrometry (MS) is one of the most powerful bioanalytical tools available to researchers. MS-based proteomics analyse protein expressions, post-translational modifications, and disease-specific proteins. In lipidomics, MS quantifies lipids related to metabolic disorders, while in metabolomics, it identifies metabolic changes associated with diseases and therapeutic targets.

Emerging technologies like single-cell analysis and microfluidics offer high-resolution analysis of individual cells for biomarker detection. Liquid biopsies analyse biomarkers in bodily fluids non-invasively, while next-generation sequencing (NGS) allows rapid sequencing of entire genomes or specific genetic regions to detect variations or mutations. Cutting-edge approaches such as CRISPR-based diagnostics and artificial intelligence-driven analysis are also promising for identifying biomarkers. CRISPR tools allow precise detection of genetic mutations, while machine learning models can uncover complex patterns in datasets.

Validating biomarker methods is critical to ensure their reliability and clinical relevance. Platforms such as Enzyme-Linked Immunosorbent Assay (ELISA) and Meso Scale Discovery (MSD) are prevalent among the tools used to confirm biomarker presence and quantify their levels.

Due to the importance of biomarkers, there is guidance for biomarker method validation, such as the FDA’s 'BEST’ (Biomarkers, Endpoints, and other Tools) resource. This places biomarkers into categories such as diagnostic, surveillance, predictive, safety, and prognostic.

Bioanalytical techniques to monitor therapeutic responses

Bioanalysis is also used to monitor therapeutic responses by evaluating drug levels, biomarkers, and physiological changes that indicate the safety and efficacy of treatments. These bioanalytical techniques allow for real-time feedback, which guides therapeutic approaches.

Many of the same methods used to identify and verify biomarkers are also used to monitor therapeutic responses, such as immuno-assays ELISA and MSD and other testing, including liquid biopsy, NGS, PCR, FACS, and single-cell analysis. Liquid chromatography is combined with mass spectrometry (LC-MS) to quantify drug levels and metabolites, ensuring therapeutic levels are maintained and treatments remain effective and safe.

There are many methods to monitor therapeutic responses in personalised medicine. They can be complex and, without adequate experience, overwhelming. Knowing which method should be used to best monitor a specific therapeutic response is one of the critical skills a qualified laboratory testing partner can bring to a drug development programme.

Challenges of PK and PD studies

Pharmacokinetic (PK) and pharmacodynamic (PD) studies are critical tools for understanding, characterising, and predicting drug exposure and response. PK studies explore how a drug is absorbed, distributed, metabolised, and excreted (ADME) in the body, while PD studies focus on the biochemical and physiological effects of drugs and their mechanisms of action.

PD measurements generate biomarker data, which is used to develop personalised treatment specifications, including dosage optimisation, real-time therapy monitoring, and treatment response prediction. PK and PD studies also facilitate smarter trial design during personalised drug development by helping to identify responsive subpopulations.

The challenges in PK and PD studies for precision medicine drug development include the variability of factors between patients, the complexity of drug behaviour, and data collection.

Individual variability

Factors such as genetics, age, weight, and comorbidities create drastic variability in drug ADME, complicating the optimisation of dosing levels in diverse populations. However, population pharmacokinetics (popPK) models help guide dose adjustments, and physiologically based pharmacokinetics (PBPK) can simulate drug behaviour under different conditions, such as pregnancy or kidney disease, making it easier to design personalised drugs for diverse populations.

Complexity of drug behaviour

Non-linear PK/PD relationships and complex ADME processes make it more difficult to predict therapeutic outcomes. A drug with a long half-life or slow absorption can make accurate modelling tricky. To counter this challenge, PBPK models and advanced compartmental analysis can provide more detailed simulations, and AI and machine learning can enhance PK/PD dynamics prediction.

Data limitations

PK data collection in clinical trials can be complex due to ethical, logistical, and medical restraints, particularly in late-phase or paediatric trials. If sampling is sparse in either PK or PD studies, models can be less accurate, making it harder to predict drug behaviour. Solutions include wearable automatic sampling systems or dried blood spot techniques, which both collect higher-quality PK data more frequently.

Regulatory challenges

The novel and diverse nature of personalised medicine creates challenges for drug developers and regulators alike.

Data produced from bioanalysis forms the cornerstone of any regulatory submission, making it vital that developers get it right. But developing precision medicine treatments is a complex task. Validating biomarkers is scientifically and technically demanding and requires robust evidence to prove clinical utility. Aligning drug and diagnostic approval is another difficult endeavour. Parallel review processes, where pharmaceuticals and companion diagnostics are evaluated simultaneously, can save time and ensure both components are designed to work together. However, these workflows are not always in place.

Personalised medicine requires handling sensitive biological information, which adds another layer of regulatory complexity. To ensure patient information is adequately protected, regulators must adopt best practices in areas such as data handling and security.

The differing regulatory requirements across countries and continents also complicate the global rollout of personalised therapies. Greater international collaboration is needed to harmonise regulatory standards. Regulatory bodies should also consider incorporating real-world evidence (RWE) into the approval process. RWE can play a crucial role in supporting the efficacy and safety of personalised medicines and helping illuminate the long-term impacts of many treatments.

Finally, it’s important to remember that regulators don’t have all the answers, particularly in evolving fields like personalised medicine. Authorities and developers learn from each other as new submissions arrive, new testing data emerges, and new use cases are explored. For this reason, the industry and regulators must work together to ensure the safest and most effective drugs make their way to market.

A final word

Personalised medicine has become a significant contributor to healthcare and promises to save countless lives in the future. To ensure its development is as smooth, safe, and rapid as possible, the drug development community must use the most cutting-edge bioanalysis techniques.

This places great importance on those carrying out the tests. Developers should ensure they work only with the most knowledgeable and experienced professionals capable of designing the testing they need while appeasing fast-shifting regulatory requirements.