Personalized Medicine: Where Are We Now, What Comes Next?
Personalized medicine may be described as tailoring medical treatment to the individual characteristics, needs, and preferences of a patient during all stages of care, from prevention and diagnosis to treatment and follow-up. This medical model proposes the customization of medicine – with medical decisions, practices, and products being personalized for each patient.
The use of genetic information has played a major role in certain aspects of personalized medicine. It is becoming more common for doctors to test for gene variants before prescribing certain drugs. For example, children with leukemia might get the TPMT gene test to help doctors choose the right dosage of medicine to prevent toxic side effects. Some HIV-infected patients are severely allergic to treatment drugs, and genetic tests can help identify who can safely take the medicines.
There are many benefits associated with the greater accessibility of genetic information, and decoding genomes will increase our understanding of the genetic make-ups of diseases. DNA extraction and analysis used to cost up to $1 million U.S. But DNA sequencers and data analysis could bring that cost down below $1,000, according to MKI, one of Japan’s most prominent technology consultancies specializing in bioinformatics.
Pharmacogenomics – the study of how genes affect the way medicines work in the body – is frequently used for cancer treatment. Some breast cancer drugs only work in women with particular genetic variations. If testing shows patients with advanced melanoma (skin cancer) have certain variants, two new approved drugs can treat them. Further examples of successful therapies include BiDil, used in addition to routine medicines to treat heart failure in African American patients. Other examples of personalized therapies include drugs aimed at molecular targets specific to a patient’s disease state, such as adydeco for cystic fibrosis and Zykadia for melanoma, and immunotherapies that combat tumors using the body’s own immune system.
The implications for life sciences companies
Under the personalized medicine model, drugs could be tailored to a group of patients’ profiles, dramatically improving efficacy rates and reducing the costs and complications associated with one-size-fits-all medications. For life science companies, this approach has the potential to improve sales and profits through a new business model: differentiated products for segmented populations.
For life sciences companies, determining if they would like to make necessary investments in personalized medicine is a strategic decision. The steep costs required seem out of proportion to the small markets for each drug, with the exception of cancer-treatment drugs. Furthermore, the technologies required to identify and quantify all the molecular markers and mutations linked to specific diseases are still in their infancy. While the cost of sequencing the human genome has decreased, the analysis needed to interpret the data is still a challenge for many companies.
According to Eric Lai, the head of pharmacogenomics at Takeda Pharmaceuticals, part of the largest drug maker in Asia, life sciences companies have been doing it all wrong when it comes to advancing personalizing therapies. Development shouldn’t start with the drug – it should begin with the patient. Lai advocates using large databases to first identifying the molecularly defined patient groups in need of effective treatments and then working backwards.
As genome sequencing becomes more and more affordable and even proteomes can be analyzed more and more quickly, new correlations can be found faster – such as the effect of specific therapies for dedicated genome mutations, which again can provide enormous opportunities to explore complex interrelationships of the human metabolism. Genomes, transcriptomes, proteomes, phenotypes – the amount of data for personalized medicine is growing at breathtaking pace.
Harvesting and utilizing this ever-growing amount of data can enable life sciences companies to make sound decision in earlier R&D stages than ever before, which means saving cost and time to market.
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This blog was written jointly by Jasjeet Singh and me. I would like to express to Jasjeet my appreciation for his inputs, research, and contribution and thank him for his fantastic engagement!