Distinguishing prognostic and predictive biomarkers: an information theoretic approach.

Motivation: The identification of biomarkers to support decision-making is central to personalized medicine, in both clinical and research scenarios. The challenge can be seen in two halves: identifying predictive markers, which guide the development/use of tailored therapies; and identifying prognostic markers, which guide other aspects of care and clinical trial planning, i.e.

Data Science Solution to Event Prediction in Outsourced Clinical Trial Models.

Late phase clinical trials are regularly outsourced to a Contract Research Organisation (CRO) while the risk and accountability remain within the sponsor company. Many statistical tasks are delivered by the CRO and later revalidated by the sponsor. Here, we report a technological approach to standardised event prediction.

Challenges in the prediction and modeling of oral absorption and bioavailability.

To predict the performance of a drug following oral dosing, a thorough understanding of the dissolution, uptake and metabolism of the compound is required. In this review, approaches to in silico modeling of these processes are discussed. Although oral absorption, which is limited by dissolution and passive permeation, is to some extent predictable, bioavailability, which is influenced by first-pass metabolism in the intestines and liver, is much more difficult to predict.