Therapeutic drug monitoring in personalised medicine revolutionises patient careTherapeutic drug monitoring in personalised medicine revolutionises patient care

The era of generic prescriptions and standardised treatment plans is rapidly giving way to a new paradigm in healthcare: personalised medication management. This innovative approach recognises the inherent individuality of each patient, acknowledging that factors like genetic makeup, lifestyle choices, concurrent medications, and underlying health conditions can profoundly influence how a person responds to a particular drug.

In essence, personalised medication management moves beyond the traditional "one-size-fits-all" model, seeking to optimise treatment outcomes by tailoring medication choices, dosages, and schedules to each individual's specific needs and characteristics. A cornerstone of this personalised approach is therapeutic drug monitoring (TDM), a powerful tool that allows healthcare providers to precisely measure drug concentrations in a patient's bloodstream.

This real-time feedback enables clinicians to fine-tune medication regimens, ensuring that drug levels remain within the therapeutic window for maximum efficacy and minimal risk of adverse events. Integrating TDM into personalised medicine revolutionises patient care, promising safer, more effective, and truly individualised treatment plans.

Understanding therapeutic drug monitoring: the cornerstone of personalised medicine

Therapeutic drug monitoring (TDM) is the cornerstone of personalised medicine. By tailoring medication regimens to individual variations in drug disposition and response, TDM not only facilitates drug development but also significantly improves economic and health outcomes. These include shorter hospital stays, maximised therapeutic effects, and minimised adverse effects, making it a valuable tool in healthcare.

TDM is particularly valuable for drugs with a narrow therapeutic index, such as chemotherapy agents, where even slight fluctuations in drug levels can have serious consequences. However, its versatility extends beyond this, as it also provides insights into doses with unpredictable serum concentrations and helps identify the potential for severe or life-threatening toxicities. Currently, TDM is commonly employed for a wide range of medications, including chemotherapy, cardioactive drugs, bronchodilators, antiepileptics, antipsychotics, antibiotics, antiretrovirals, antimetabolites, and immunosuppressants, making it a comprehensive tool in healthcare.

TDM in oncology: a critical tool for personalised cancer care, urgently needed

In the field of oncology, where the variable effectiveness of standard treatment strategies presents a significant challenge, TDM plays a critical role. By considering individual genotypes and disease histories alongside traditional factors like environment, lifestyle, and family history, TDM offers a promising approach to improving cancer treatment outcomes.

TDM is crucial in optimising cancer patient dosages, particularly in paediatric oncology, where developmental physiology and body composition can lead to significant variations in pharmacokinetic (PK) and pharmacodynamic (PD) parameters. By tailoring medication regimens based on TDM results, healthcare providers can achieve more accurate dosing and reduce the risk of suboptimal therapy or increased toxicity in children.

While TDM is well-established in some areas of medicine, its use in paediatric oncology and other paediatric specialities is still evolving. Studies have highlighted the need for increased awareness and education among healthcare professionals regarding the benefits and applications of TDM in children. As technology advances and research continues to uncover the complexities of drug responses in paediatric populations, we expect to see further integration of TDM into personalised medication management strategies for children.

Bayesian modelling in TDM: a leap forward in personalised medication management

The advent of Bayesian modelling in TDM has marked a significant leap forward in personalised medication management. This statistical approach integrates prior knowledge about a drug's pharmacokinetics and pharmacodynamics with individual patient data, resulting in more accurate and precise dosage predictions. Bayesian models can adapt and update as new information becomes available, enabling real-time adjustments to treatment plans based on individual patient responses. This dynamic approach has the potential to further refine personalised medication management, particularly in complex cases where traditional TDM approaches may fall short.

TDM technologies: from immunoassays to chromatography

Various technologies are employed in TDM, each with its strengths and limitations. Immunoassays, including enzyme-multiplied immunoassay technique (EMIT) and fluorescence polarisation immunoassay (FPIA), are widely used for their speed and simplicity. Chromatographic methods, such as high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS/MS), offer greater sensitivity and specificity, making them ideal for complex drug matrices or when measuring multiple drugs simultaneously. The choice of technology often depends on the specific drug being monitored, the desired turnaround time, and the available resources.  

The future of personalised medicine and TDM

The future of healthcare lies in personalised medicine, and TDM is an integral part of this paradigm shift. As our understanding of genetics, drug interactions, and individual patient responses continues to grow, so will the potential for TDM to revolutionise how we treat and manage a wide range of diseases. By tailoring medication regimens to each patient's unique needs, we can improve treatment outcomes, minimise adverse effects, and ultimately enhance the quality of life for individuals worldwide.

Dr. Mohamed Nagy is a Pharmacy Director, Head of Personalised Medication Management, Vice Chair of the Global PGx Committee, Pharmacogenomics Global Research Network (PGRN) and an African Pharmacogenomics Network Board Member (APN).