The future of rare disease drug development: the rare disease cures accelerator data analytics platform (RDCA-DAP).

Rare disease drug development is wrought with challenges not the least of which is access to the limited data currently available throughout the rare disease ecosystem where sharing of the available data is not guaranteed. Most pharmaceutical sponsors seeking to develop agents to treat rare diseases will initiate data landscaping efforts to identify various data sources that might be informative with respect to disease prevalence, patient selection and identification, disease progression and any data projecting likelihood of patient response to therapy including any genetic data. Such data are often difficult to come by for highly prevalent, mainstream disease populations let alone for the 8000 rare disease that make up the pooled patient population of rare disease patients.

A population pharmacokinetic-pharmacodynamic model evaluating efficacy of nalbuphine extended-release in patients with prurigo nodularis.

Aims: Population pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) models were used to describe the exposure-response (E-R) relationship between nalbuphine exposure and two widely used rating scales for itch: the Numerical Rating Scale for the subject's 'average'; itch experience (NRS-AV) and the Worst Itch (WI-NRS), with 24-h recall. Simulations based on the model E-R relationship were used to support dose selection for Phase 3 clinical trials and were evaluated with a target of reducing the 7-day average of the 24-h WI-NRS by at least 30% from baseline in most of the analysis population.

Methods: Data from two clinical trials (NCT02373215: 9 healthy subjects; NCT02174419: 62 subjects with PN), in patients with prurigo nodularis (PN) with moderate to severe itch who received treatment with either of two doses of nalbuphine extended release (ER) or placebo, were used for the analysis.

Usability of a novel digital medicine system in adults with schizophrenia treated with sensor-embedded tablets of aripiprazole.

Objective: Digital medicine system (DMS) is a novel drug-device combination that objectively measures and reports medication ingestion. The DMS consists of medication embedded with an ingestible sensor (digital medicine), a wearable sensor, and software applications. This study evaluated usability of the DMS in adults with schizophrenia rated by both patients and their health care providers (HCPs) during 8-week treatment with prescribed doses of digital aripiprazole.

Optimization of a Digital Medicine System in Psychiatry.

Background: Nonadherence to medication compromises the effectiveness of psychiatric treatments in patients with serious mental illness (SMI). A newly developed digital medicine system (DMS) offers an opportunity to objectively assess and report patient medication adherence. DMS includes a wearable sensor that receives a data signal from a medication tablet with an embedded ingestible sensor after ingestion of the medication and transmits that data to the patient's mobile device to display health care information for the patient and treatment team.

Developing a Digital Medicine System in Psychiatry: Ingestion Detection Rate and Latency Period.

Background: A digital medicine system (DMS) has been developed to measure and report adherence to an atypical antipsychotic, aripiprazole, in psychiatric patients. The DMS consists of 3 components: ingestible sensor embedded in a medication tablet, wearable sensor, and secure mobile and cloud-based applications. An umbrella study protocol was designed to rapidly assess the technical performance and safety of the DMS in multiple substudies to guide the technology development.