Establishing Medical Intelligence - Leveraging FHIR to Improve Clinical Management: a retrospective cohort and clinical implementation study.

BACKGROUND: FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data. OBJECTIVE: Here, we designed and implemented a conceptual medical intelligence framework to leverage real-world care data for clinical decision-making. METHODS: A Python package for the utilization of multimodal FHIR data (FHIRPACK) was developed and pioneered in five real-world clinical use cases, i.e., myocardial infarction (MI), stroke, diabetes, sepsis, and prostate cancer (PC). Patients were identified based on ICD-10 codes, and outcomes were derived from laboratory tests, prescriptions, procedures, and diagnostic reports. Results were provided as browser-based dashboards. RESULTS: For 2022, 1,302,988 patient encounters were analyzed. MI: In 72.7% of cases (N=261) medication regimens fulfilled guideline recommendations. Stroke: Out of 1,277 patients, 165 patients received thrombolysis and 108 thrombectomy. Diabetes: In 443,866 serum glucose and 16,180 HbA1c measurements from 35,494 unique patients, the prevalence of dysglycemic findings was 39% (N=13,887). Among those with dysglycemia, diagnosis was coded in 44.2% (N=6,138) of the patients. Sepsis: In 1,803 patients, Staphylococcus epidermidis was the primarily isolated pathogen (N=773, 28.9%) and piperacillin/tazobactam was the primarily prescribed antibiotic (N=593, 37.2%). PC: Three out of 54 patients who received radical prostatectomy were identified as cases with PSA persistence or biochemical recurrence. CONCLUSIONS: Leveraging FHIR data through large-scale analytics can enhance healthcare quality and improve patient outcomes across five clinical specialties. We identified i) sepsis patients requiring less broad antibiotic therapy, ii) patients with myocardial infarction who could benefit from statin and antiplatelet therapy, iii) stroke patients with longer than recommended times to intervention, iv) patients with hyperglycemia who could benefit from specialist referral and v) PC patients with early increases in cancer markers.

Pharmacokinetics and Determination of Tumor Interstitial Distribution of a Therapeutic Monoclonal Antibody Using Large-Pore Microdialysis.

R7072 is a fully human monoclonal antibody (mAb) exerting anti-tumor activity via blockade of insulin like growth factor 1 receptor. The tumoral interstitial concentrations are anticipated to be better surrogates of active site concentrations than commonly used serum concentrations for pharmacokinetic-pharmacodynamic correlation of anti-tumor mAbs. Previously, a large-pore microdialysis technique for measuring tissue interstitial concentrations of R7072 in non-tumor bearing mice was established. In the current studies, the serum pharmacokinetics of R7072 were assessed and tissue interstitial concentrations were measured by large-pore microdialysis following intravenous and intraperitoneal administration of R7072 in tumor bearing mice. R7072 exhibited nonlinear pharmacokinetics in the studied dose range. Tumor and subcutaneous interstitial concentration data suggested some delay in tissue distribution after dosing. A dose-dependent increase in the ratio of tumor interstitial to serum concentration was observed indicating target-mediated drug disposition in tumor tissue. However, subcutaneous interstitial to serum concentration ratios were similar across the doses as observed previously in non-tumor bearing mice. A two-compartment population pharmacokinetic model with subcutaneous and tumor as open-loop compartments comprising of parallel linear and non-linear elimination from serum, linear disposition from subcutaneous interstitium and non-linear disposition from tumor interstitium was developed to simultaneously describe the pharmacokinetic data from all matrices.

Butler enables rapid cloud-based analysis of thousands of human genomes.

We present Butler, a computational tool that facilitates large-scale genomic analyses on public and academic clouds. Butler includes innovative anomaly detection and self-healing functions that improve the efficiency of data processing and analysis by 43% compared with current approaches. Butler enabled processing of a 725-terabyte cancer genome dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project in a time-efficient and uniform manner.

Model-Based Assessments of CYP-Mediated Drug-Drug Interaction Risk of Alectinib: Physiologically Based Pharmacokinetic Modeling Supported Clinical Development.

Alectinib is a selective anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive non-small cell lung cancer. Alectinib and its major active metabolite M4 exhibited drug-drug interaction (DDI) potential through cytochrome P450 (CYP) enzymes CYP3A4 and CYP2C8 in vitro. Clinical relevance of the DDI risk was investigated as part of a rapid development program to fulfill the breakthrough therapy designation. Therefore, a strategy with a combination of physiologically based pharmacokinetic (PBPK) modeling and limited clinical trials focused on generating informative data for modeling was made to ensure extrapolation ability of DDI risk. The PBPK modeling has provided mechanistic insight into the low victim DDI risk of alectinib through CYP3A4 by a novel two-dimensional analysis for fmCYP3A4 and FG , and demonstrated negligible CYPs 2C8 and 3A4 enzyme-modulating effects at clinically relevant exposure. This work supports that alectinib can be prescribed without dose adjustment for CYP-mediated DDI liabilities.

Reduced physiologically-based pharmacokinetic model of repaglinide: impact of OATP1B1 and CYP2C8 genotype and source of in vitro data on the prediction of drug-drug interaction risk.

PURPOSE: To investigate the effect of OATP1B1 genotype as a covariate on repaglinide pharmacokinetics and drug-drug interaction (DDIs) risk using a reduced physiologically-based pharmacokinetic (PBPK) model. METHODS: Twenty nine mean plasma concentration-time profiles for SLCO1B1 c.521T>C were used to estimate hepatic uptake clearance (CLuptake) in different genotype groups applying a population approach in NONMEM v.7.2. RESULTS: Estimated repaglinide CLuptake corresponded to 217 and 113 µL/min/10(6) cells for SLCO1B1 c.521TT/TC and CC, respectively. A significant effect of OATP1B1 genotype was seen on CLuptake (48% reduction for CC relative to wild type). Sensitivity analysis highlighted the impact of CLmet and CLdiff uncertainty on the CLuptake optimization using plasma data. Propagation of this uncertainty had a marginal effect on the prediction of repaglinide OATP1B1-mediated DDI with cyclosporine; however, sensitivity of the predicted magnitude of repaglinide metabolic DDI was high. In addition, the reduced PBPK model was used to assess the effect of both CYP2C8*3 and SLCO1B1 c.521T>C on repaglinide exposure by simulations; power calculations were performed to guide prospective DDI and pharmacogenetic studies. CONCLUSIONS: The application of reduced PBPK model for parameter optimization and limitations of this process associated with the use of plasma rather than tissue profiles are illustrated.

Cyclosporine inhibition of hepatic and intestinal CYP3A4, uptake and efflux transporters: application of PBPK modeling in the assessment of drug-drug interaction potential.

PURPOSE: To apply physiologically-based pharmacokinetic (PBPK) modeling to investigate the consequences of reduction in activity of hepatic and intestinal uptake and efflux transporters by cyclosporine and its metabolite AM1. METHODS: Inhibitory potencies of cyclosporine and AM1 against OATP1B1, OATP1B3 and OATP2B1 were investigated in HEK293 cells +/- pre-incubation. Cyclosporine PBPK model implemented in Matlab was used to assess interaction potential (+/- metabolite) against different processes (uptake, efflux and metabolism) in liver and intestine and to predict quantitatively drug-drug interaction with repaglinide. RESULTS: Cyclosporine and AM1 were potent inhibitors of OATP1B1 and OATP1B3, IC(50) ranging from 0.019-0.093 µM following pre-incubation. Cyclosporine PBPK model predicted the highest interaction potential against liver uptake transporters, with a maximal reduction of >70% in OATP1B1 activity; the effect on hepatic efflux and metabolism was minimal. In contrast, 80-97% of intestinal P-gp and CYP3A4 activity was reduced due to the 50-fold higher cyclosporine enterocytic concentrations relative to unbound hepatic inlet. The inclusion of AM1 resulted in a minor increase in the predicted maximal reduction of OATP1B1/1B3 activity. Good predictability of cyclosporine-repaglinide DDI and the impact of dose staggering are illustrated. CONCLUSIONS: This study highlights the application of PBPK modeling for quantitative prediction of transporter-mediated DDIs with concomitant consideration of P450 inhibition.