Personalized pharmaceutical medications
One of the questions our organization is actively working on within the U.S., as well as in several other countries we service, is how can we better utilize next generations testing and patient specific factors (enzymes, allergies, lifestyle etc.) to allow us avenues to better personalize the pharmaceutical medications we develop and supply to enhance compliance, lessen side effects and improve the patient's outcomes when they are starting new medication therapies?
The term "personalized medicine" might be applied to may situations. When a physician prescribes a drug and instructs patients about the dose and dosing schedule, the medication is "personalized". A compounding pharmacy could make e.g. tablets based on a given patient's weight, so that the dose per kg is as it should be, hence the medicine would be personalized. However, it is often the case that the condition itself is idiopathic, and the physician knows only the symptoms and not its cause, such as in case of high blood pressure. If the first medication does not work, physician tries another until he finds one that works; the medication is then "personalized".
However, the "personalized medicine" as is understood in the current "precision medicine" initiative in the USA is a drug / therapy that is based on a detail understanding of the patient's condition / disease - its mechanism, location of the target, a clear definition of the molecular target, etc., - so that medication can actual made personalized so that the biological cause of the disease, and its targets, can be addressed.
In many cases, medical knowledge is as yet not adequate to enable such approach. In cancer, for example, we can diagnose the disease, and when diagnosed early it can often be treated successfully. However, we do not know exactly the primary step in cancer initiation, and hence cannot addressed it at that stage. We largely go by environmental effects, may try to avoid such effects, but "cancer prevention" is still a "pie in the sky". (cf. K. Petrak: Cancer prevention - how well is this working?, accepted for publication).
As it it is now, I recommend that when the topic of "personalized medicine" is discussed, it is defined up front what type and extent of personalization is intended.
A few years ago, 2 Doctors who specialize in treating cancer patients developed a "cancer cure" that was tailored to only one specific patients type of rare cancer. They used T-cells, helper cells and the patients DNA through genomic testing to reprogram the patients cancer cells DNA. This caused the "reprogrammed" cancer cells to attack and kill only the other cancer cells. After a year of treatment, the patient was deemed cancer free. So in this case this is "personalized medicine". http://www.hopkinsmedicine.org/news/publications/promise_progress/files/sebindoc/s/m/D2C90EFA485C954E8E29280292984C5C.pdf
The holy grail of any genomic screening testing program is intended for couples to better understand their risk of producing children with severe early onset disorders is a present and real danger. Testing services should help detect rare, family, or sub-population specific variants which could have a deleterious effect in offspring. The promise is that through advanced genomic screening human suffering can be avoided and the coinciding healthcare costs.
Patients seeking a more thorough genomic review versus common, standard carrier screening panels which focus solely on known pathogenic variants, require better samples, equipment, bioinformatic data and report analysis.
The desired linkage between a genomic screen and a validated, FDA-cleared pharmaceutical treatment regime is a challenge at this time. Any suggested drug program should be framed within the current state of medical practice, not speculation or false hopes.
Successful development of personalized / precision medicine will depend critically on major improvements in the following areas, and in this order:
- understanding of disease at the level of biological mechanisms, not symptoms. Specific molecular structures that are involved in the very early, initiation stages of the disease must be determined;
- pharmaceutical research and development must utilize this new knowledge in developing new drugs that target such relevant molecular targets (and not the general biological pathways;
- medical education and practice need to move away from practicing "medical arts" and move towards understanding and applying medical science.
And perhaps we might even consider whether health care costs / compensation should be scaled according how successful treatments actually are...
For the pharmaceutical industry personalized medicine presents both challenges and opportunities. Many pharmaceutical companies have committed to the vision of ‘right drug, right patient, right time‘, particularly in therapeutic areas such as oncology and neuroscience. Pharma recognizes that this strategy provides the opportunity to achieve substantial clinical advances in specific patient populations, compared with currently available “non-specific” medications which is expected to create a compelling value proposition and to facilitate reimbursement by payers. Drug development timelines could be accelerated, and success rates improved by conducting trials in molecularly selected patient populations that result in more rapid proof of concept and more robust clinical outcomes, allowing smaller Phase 3 trials. However, there are also significant challenges. Most importantly, the drug developer must be able to achieve the requisite return on investment despite the restricted market size. In addition, drug development costs may be increased due to the complexities of biomarker analysis and diagnostic development. Molecular profiling is an emerging science, and several large and expensive drug development programs have faltered due to the selection of the wrong biomarker to guide patient selection. Trials involving biomarkers are attracting high interest from researchers, but require new competencies in trial design, data analysis and investigator expertise in sample collection and management. Greater statistical and computing power is necessary to mine data for complicated relationships between genetic, biological and environmental factors.