Personalized medicine is truly that. Based on an individual’s unique genetic profile, it’s used to make precise decisions according to that person’s biologically predetermined factors, and guides the optimum approach to disease prevention, diagnosis, and treatment. It determines the most appropriate medications or therapies and administers them. It guarantees delivery of the right drug and the right dosage at precisely the right time, tailored specifically for that person.
Cell and gene therapies have been subjects of research for some time, but they have gained traction in real-world modern medicine and are projected to reach a market value of more than $36 billion by 2027. Future growth will be driven by factors that include payers mandating the use of such diagnostics, increased understanding of drug safety, advancements in quality of diagnostic testing, and more informed and proactive consumers. As we continue to learn even more about individuals through genome sequencing and the integration of that data into medical records, personalized medicine has the potential to revolutionize the field.
This approach, however great its promise for prediction, diagnosis, minimized side effects, and improving outcomes, has its challenges. Those include collecting and analyzing large amounts of data, security, privacy, and the speed at which that data must be processed. Add to that training healthcare workers, access to high-quality artificial intelligence (AI) and machine learning (ML) technology, and ethical implications around the potential impact of precision-based treatments on overall population health.
Efficient clinical and commercial manufacturing presents issues, too. Drug developers must establish stable, robust, and cost-efficient manufacturing processes, especially for rare disorders with small patient populations and limited therapeutic options. Ensuring the anonymization and security of ever-expanding warehouses of data poses significant ethical concerns. Meanwhile, the turnaround time for data analysis, especially in acute care settings, is a factor that demands that the validity and reliability of such data be unquestioned.
To overcome these challenges and achieve the full potential of personalized medicine, several measures must be taken. Building capacity in terms of robust laboratory infrastructure is essential. While the development of intelligent big data platforms and the collection of uniformly structured data will facilitate efficient analysis and interpretation, patient and community involvement are also crucial for sustainable and successful initiatives around personalized medicine.
Nonetheless, personalized medicine presents game-changing opportunities to move away from a one-size-fits-all approach in diagnostics, drug therapy, and prevention. Its great promise lies in a near total rethinking about developing and delivering healthcare through genuinely individualized approaches that are more precise and efficient, which presents a sea change in the expansion of quality control, concerns and measures.
As investments are made in capacity building, infrastructure and stakeholder engagement, consideration must be given to ensuring quality throughout an entirely new range of processes that must meet compliance and regulatory standards. Drug development companies must embrace new technology. That technology, and those companies, will not only pave the way for a future in which personalized medicine becomes the standard of care that benefits patients worldwide, they will elevate access and outcomes through an ongoing dedication to quantifiable, verifiable, and unquestionable quality.