RESUMEN
Machine learning (ML) models were applied to pharmacovigilance (PV) data in a two-component proof-of-concept study. PV data were partitioned into Training, Validation, and Holdout datasets for model training and selection. During the first component ML models were challenged to identify factors in individual case safety reports (ICSRs) involving spinosad and neurological and ocular clinical signs. The target feature for the models were these clinical signs that were disproportionately reported for spinosad. The endpoints were normalized coefficient values representing the relationship between the target feature and ICSR free text fields. The deployed model accurately identified the risk factors "demodectic," "demodicosis," and "ivomec." In the second component, the ML models were trained to identify high quality and complete ICSRs free of confounders. The deployed model was presented with an external Test dataset of six ICSRs, one that was complete, of high quality, and devoid of confounders, and five that were not. The endpoints were model-generated probabilities for the ICSRs. The deployed ML model accurately identified the ICSR of interest with a greater than 10-fold higher probability score. Although narrow in scope, the study supports further investigation and potential application of ML models to animal health PV data.
Asunto(s)
Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos , Animales , Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos/veterinaria , Farmacovigilancia , Aprendizaje AutomáticoRESUMEN
Statistical algorithms for detecting safety signals are beginning to be applied to Animal Health Pharmacovigilance (PV) databases. How these signal detection algorithms (SDAs) perform in an animal health PV database is the subject of this report. Statistical methods and SDAs were assessed against a set of known signals in order to identify which SDAs were most appropriate for signal detection using the Elanco Animal Health PV database. A reference set of adverse events that should signal was created for 31 products across four species. Nine SDAs based on five disproportionality statistical methods were evaluated against the reference set. The performance metrics were sensitivity, precision, specificity, accuracy, and F score. For bovine and porcine products, the Observed-to-Expected (O/E) SDA was the closest in terms of geometric distance to 100% sensitivity and 100% precision. For canine and feline products, the Information Component (IC) SDA was geometrically closest to 100% sensitivity and 100% precision. Principal Component Analysis confirmed that the O/E and IC SDAs were unique performers with respect to one another and other SDAs. The performance of the SDAs was dependent on the choice of the statistical method with differences seen between animal species.
Asunto(s)
Sistemas de Registro de Reacción Adversa a Medicamentos , Algoritmos , Interpretación Estadística de Datos , Bases de Datos Farmacéuticas , Farmacovigilancia , Animales , Animales Domésticos , Análisis de Componente Principal , Especificidad de la EspecieRESUMEN
Increased glucose flux through the polyol pathway and the resultant oxidative stress is thought to be a major mechanistic contributor to microvascular diabetic complications. Inhibition of flux through this pathway can be blocked through inhibition of either of 2 enzymes, aldose reductase (AR) or sorbitol dehydrogenase (SDH). This report describes the pharmacokinetics, biomarker pharmacodynamics, and safety of CP-642,931, a potent and specific sorbitol dehydrogenase inhibitor (SDI). CP-642,931 was administered for 7 days to 57 healthy volunteers in doses ranging from 1 to 35 mg daily. After the 35-mg dose, CP-642,931 showed a t((1/2)) of 20.1 hours and t(max) at 0.5 to 1.25 hours. After a 35-mg dose, maximum inhibition of SDH was 91% (on days 1 and 7), and maximum serum sorbitol increase was 152-fold on day 7 compared to control. Five participants discontinued the study due to adverse events, including myalgia, muscle spasm, and muscle fatigue. All symptoms resolved in all but 1 participant, who continued to report intermittent muscle fasciculations upon follow-up. In conclusion, CP-642,931 is a potent and specific SDI that is rapidly absorbed through the oral route and effectively inhibits SDH. However, the drug is not well tolerated due to adverse neuromuscular effects.