Phenotypic response surfaces

Modern medical practice since its inception in the early 19th to 20th centuries has been seen as "a science of uncertainty and art of probability" as mused by one of its founders, Sir William Osler. The lack of a concrete mechanism for the relationship between drug dosing and its efficacy led largely to the use of population averages as a metric for determine optimal doses for patients. This issue is further compounded by the introduction of combination therapies as there is an exponential growth in number of possible combinations and outcomes as the number of drugs increases. In 2011 the PRS methodology was developed by a team led by Dr. Ibrahim Al-Shyoukh and Dr. Chih Ming Ho of the University of California Los Angeles to provide a platform that would allow for a comparatively small number of calibration tests to optimize multi-drug combination therapies based on measurement of cellular biomarkers. The PRS platform has since become the basis for a commercial optimization platform marketed by Singapore based Kyan Therapeutics in partnership with Kite Pharma and the National University of Singapore to provided personalized combination therapies for oncological applications. == Methodology ==

The PRS platform utilizes a neural network to fit data sets to a regression function resulting in a parabolic surface that provides a direct quantitative relationship between drug dose and efficacy. The governing function for the PRS platform is given as the following: E(C,t) = x_0 + \sum_{i=1}^Mx_iC_i +\sum_{i=1}^My_{ii}C_i^2 +\sum_{i=1}^{M-1}\sum_{j=i+1}^Mz_{ij}C_iC_j where: • E is the combination efficacy as a function drug dose and time, given as a biomarker value • C is the drug dose • t is time • x, y, z are PRS coefficients representing drug interaction • M is the number of drugs The parabolic nature of the relationship allows for the minimal required calibration test to utilize the PRS regression in the search area of NM combinations, where N is the number of dosing regimens and M is the number of drugs in the combination. == Applications ==

The mechanism-independent nature of the PRS platform makes it utilizable to treat a broad spectrum of diseases including for the treatment of cancers, infectious diseases, and for organ transplants. PRS based therapy has been used to successful derive an optimized 3 drug combination to treat multiple myeloma and overcome drug resistance. Infectious disease Drug resistance is a particular challenge when attempting to treat infectious diseases as monotherapy solutions carry the risk of increasing drug resistance and combination therapy demonstrates lower mortality rates. Highly contagious infectious diseases like tuberculosis have become the leading cause of death by infectious disease world wide. The platform identified the metabolite EIDD-1931 as having strong antiviral properties that can be used in combination with other commercial antiviral agents to create an effective therapy for the treatment of the SARS-CoV-2 delta variant. Organ transplant The PRS derived phenotypic personalized dosing platform developed in 2016 has been used to provide personalized tacrolimus and prednisone dosing for liver transplant procedures and post transplant care to prevent transplant rejection events. This methodology is able to use the minimal number of calibration tests and as a result provides a physicians with a rolling window in which daily optimized drug dose can be predicted. The platform is recalibrated daily to take into consideration the patients changing physiological responses to the drug regimen providing physicians with accessible personalized treatment tools and eliminating the need to use of population average based dosing. The platform is actively being considered for other transplant uses including kidney and heart transplants. == References ==