The Institute of Conservation

Endometriosis is a commonly occurring progressive gynecological disorder, in which tissues similar to the lining of the uterus grow on other parts of the female body, including ovaries, fallopian tubes, and bowel. It is one of the primary causes of pelvic discomfort and fertility challenges in women. The actual cause of the endometriosis is still undetermined. As a result, the objective of the chapter is to identify the drivers of endometriosis’ diagnoses via leveraging selected advanced machine learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a greater extent if a likelihood of endometriosis could be predicted well in advance. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Several direct and indirect features were identified as important to an accurate prediction of the condition onset, including selected diagnosis and procedure codes. Creating analytical tools based on the model results that could be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers might aid the objective of improving the diagnostic processes and result in a timely and precise diagnosis, ultimately increasing patient care and quality of life.

ObjectivesNon-randomised clinical trial designs involving comparisons against external controls or specific standards can be used to support regulatory submissions for indications in diseases that are rare, with high unmet need, without approved therapies and/or where placebo is considered unethical. The objective of this review was to summarise the characteristics of non-randomised trials submitted to the European Medicines Agency (EMA) or Food and Drug Administration (FDA) for indications in haematological cancers, haematological non-malignant conditions, stem cell transplants or rare metabolic diseases.MethodsWe conducted systematic searches of EMA databases of conditional approvals, exceptional circumstances, or orphan drug designations and FDA inventories of orphan drug designations, accelerated approvals, breakthrough therapy, fast-track and priority approvals. Products were included if reviewed by at least one agency between 2005 and 2017, the primary evidence base was non-randomised trial(s) and the indication was for haematological cancers, stem cell transplantation, haematological conditions or rare metabolic conditions.ResultsWe identified 43 eligible indication-specific products using non-randomised study designs involving comparisons with external controls, submitted to the EMA (n=34) and/or FDA (n=41). Of the 43 indication-specific products, 4 involved matching external controls to the population of a non-randomised interventional study using individual patient-level data (IPD), 12 referred to external controls without IPD and 27 did not explicitly reference external controls. The FDA approved 98% of submissions, with 56% accelerated approvals; most required postapproval confirmatory randomised controlled trials (RCT). The EMA approved 79% of submissions, with a quarter of approvals conditional on completion of a postapproval RCT or additional non-randomised trials.ConclusionsThere has been a large increase in submissions to the EMA and FDA using non-randomised study designs involving comparisons with external controls in recent years. This study demonstrated that regulators may be willing to approve such submissions, although approvals are often conditional on further confirmatory evidence from postapproval studies.

\nBackground: Aripiprazole lauroxil (AL) is a prodrug of aripiprazole, formulated as an extended-release suspension approved for the treatment of schizophrenia by injection every 4 and 6 weeks. Following completion of a Phase 1 study evaluating administration of AL every 8 weeks, a pharmacokinetic (PK) model-based approach was used to evaluate dosing scenarios likely to be encountered in clinical practice for this new dose interval.