Precision Medicine Technologies
Systematic Data-Based Approaches for Precision Medicine
Standard of care therapy for various indications, including multiple myeloma (MM), is a combination of up to 4 drugs. Precision medicine has emerged as a game changing approach for selecting drug combination tailored to an individual to avoid treatment failure in a common case of drug resistance. To maximize the therapeutic outcome with the identified combination therapy, the drug dosing strategy should undergo an analogical approach - personalized dose selection for each drug tailored to an individual. Conventional approaches - titration, additive drug design, and dose escalation – often fail to find the optimal doses. Modern approaches - predictive algorithms and genotypic modeling – require a substantial amount of data and are costly. In this pilot study, we use CURATE.AI, a disease mechanism-independent and indication agnostic platform, to create an N-of-1 drug interaction profile using only the patient’s own data to identify optimized doses. CURATE.AI has been already clinically validated and has been used to optimize combination therapy for acute lymphoblastic leukemia, combination therapy for prostate cancer, liver transplant immunosuppression, and tuberculosis therapy, among other indications.We applied CURATE.AI to retrospectively analyze medical datasetin accordance with institutional IRB. As indicated in the medical records, a patient was given 14 monthly modulated dosages of revlimid and cyclophosphamide, and a constant monthly dosage of dexamethasone. Quadratic polynomial correlation between the drugs’ dosages and the platelet count - clinical indicator of the disease progression - was used to create the patient specific CURATE.AI profile, which served as a map to identify drug dosages within clinically-accepted ranges that would result in an optimum platelet count. Using CURATE.AI analysis, the platelet count (P(r,c)) was correlated to revlimid and cyclophosphamide concentrations (r and c, respectively) by the following function: P(r,c)=62+4.690r-0.069rc+0.275r2+0.002c2, with R2value of 0.724 and a fitting correlation of 0.851. The CURATE.AI profile guided that to sustain the platelet count within the desired range (132-372x109/L), the patient should be given rabove 10 mg matched with c below 150 mg, or cabove180 mg matched with rbelow 15 mg. CURATE.AI is deterministic and does not involve any prediction or uncertainty of response. In addition, CURATE.AI recommends doses within clinically-accepted ranges at patient-specific time points to optimize treatment response for that particular patient. The ability to identify patient-specific response constants has aparadigm-shifting potential – combining precision medicine for drug selection with CURATE.AI for dose selection brings us a stop closer to true realization of personalized medicine. We have alsoinitiated a clinical trial that uses CURATE.AI for prospective dosing in MM (Clinicaltrials.gov: NCT03759093).