Factors influencing longitudinal changes of circulating liver enzyme concentrations in subjects randomized to placebo in four clinical trials.

Liver enzyme concentrations are measured as safety end points in clinical trials to detect drug-related hepatotoxicity, but little is known about the epidemiology of these biomarkers in subjects without hepatic dysfunction who are enrolled in drug trials. We studied alanine and aspartate aminotransferase (ALT and AST) in subjects randomized to placebo who completed assessments over 36 mo in a cardiovascular outcome trial [the Stabilisation of Atherosclerotic Plaque by Initiation of Darapladib Therapy ("STABILITY") trial; n = 4,264; mean age: 64.2 yr] or over 12 mo in three trials that enrolled only subjects with type 2 diabetes (T2D) [the DIA trials; n = 308; mean age: 62.4 yr] to investigate time-dependent relationships and the factors that might affect ALT and AST, including body mass index (BMI), T2D, and renal function. Multivariate linear mixed models examined time-dependent relationships between liver enzyme concentrations as response variables and BMI, baseline T2D status, hemoglobin A1c levels, and renal function, as explanatory variables. At baseline, ALT was higher in individuals who were men, <65 yr old, and obese and who had glomerular filtration rate (GFR) >60 ml·min-1·1.73 m-2. ALT was not significantly associated with T2D at baseline, although it was positively associated with HbA1c. GFR had a greater impact on ALT than T2D. ALT concentrations decreased over time in subjects who lost weight but remained stable in individuals with increasing BMI. Weight change did not alter AST concentrations. We provide new insights on the influence of time, GFR, and HbA1c on ALT and AST concentrations and confirm the effect of sex, age, T2D, BMI, and BMI change in subjects receiving placebo in clinical trials. NEW & NOTEWORTHY Clinical trials provide high-quality data on liver enzyme concentrations from subjects randomized to placebo that can be used to investigate the epidemiology of these biomarkers. The adjusted models show the influence of sex, age, time, renal function, type 2 diabetes, HbA1c, and body mass index on alanine aminotransferase and aspartate aminotransferase concentrations and their relative importance. These factors need to be considered when assessing potential signals of hepatotoxicity in trials of new drugs and in clinical trials investigating subjects with nonalcoholic fatty liver disease.

Federated electronic health records research technology to support clinical trial protocol optimization: Evidence from EHR4CR and the InSite platform.

OBJECTIVE: To determine if inclusion/exclusion (I/E) criteria of clinical trial protocols can be represented as structured queries and executed using a secure federated research platform (InSite) on hospital electronic health records (EHR) systems, to estimate the number of potentially eligible patients. METHODS: Twenty-three clinical trial protocols completed during 2011-2017 across diverse disease areas were analyzed to construct queries that were executed with InSite using EHR records from 24 European hospitals containing records of >14 million patients. The number of patients matching I/E criteria of each protocol was estimated. RESULTS: All protocols could be formalized to some extent into a medical coding system (e.g. ICD-10CM, ATC, LOINC, SNOMED) and mapped to local hospital coding systems. The median number of I/E criteria of protocols tested was 29 (range: 14-47). A median of 55% (range 38-89%) of I/E criteria in each protocol could be transformed into a computable format. The median number of eligible patients identified was 26 per hospital site (range: 1-134). CONCLUSION: Clinical trial I/E eligibility criteria can be structured computationally and executed as queries on EHR systems to estimate the patient recruitment pool at each site. The results further suggest that an increase in structured coded information in EHRs would increase the number of I/E criteria that could be evaluated. Additional work is needed on broader deployment of federated platforms such as InSite.

Augmenting THerapeutic Effectiveness Through Novel Analytics (ATHENA) - A Public and Private Partnership Project Funded by the Flemish Government (VLAIO).

The complexity and heterogeneity of cancers leads to variable responses of patients to treatments and interventions. Developing models that accurately predict patient's care pathways using prognostic and predictive biomarkers is increasingly important in both clinical practice and scientific research. The main objective of the ATHENA project is to: (1) accelerate data driven precision medicine for two use cases - bladder cancer and multiple myeloma, (2) apply distributed and privacy-preserving analytical methods/ algorithms to stratify patients (decision support), (3) help healthcare professionals deliver earlier and better targeted treatments, and (4) explore care pathway automations and improve outcomes for each patient. Challenges associated with data sharing and integration will be addressed and an appropriate federated data ecosystem will be created, enabling an interoperable foundation for data exchange, analysis and interpretation. By combining multidisciplinary expertise and tackling knowledge gaps in ATHENA, we propose a novel federated privacy preserving platform for oncology research.

Real world big data for clinical research and drug development.

The objective of this paper is to identify the extent to which real world data (RWD) is being utilized, or could be utilized, at scale in drug development. Through screening peer-reviewed literature, we have cited specific examples where RWD can be used for biomarker discovery or validation, gaining a new understanding of a disease or disease associations, discovering new markers for patient stratification and targeted therapies, new markers for identifying persons with a disease, and pharmacovigilance. None of the papers meeting our criteria was specifically geared toward novel targets or indications in the biopharmaceutical sector; the majority were focused on the area of public health, often sponsored by universities, insurance providers or in combination with public health bodies such as national insurers. The field is still in an early phase of practical application, and is being harnessed broadly where it serves the most direct need in public health applications in early, rare and novel disease incidents. However, these exemplars provide a valuable contribution to insights on the use of RWD to create novel, faster and less invasive approaches to advance disease understanding and biomarker discovery. We believe that pharma needs to invest in making better use of Electronic Health Records and the need for more precompetitive collaboration to grow the scale of this 'big denominator' capability, especially given the needs of precision medicine research.