Sterilization of Polymeric Implants: Challenges and Opportunities.

Degradable and environmentally responsive polymers have been actively developed for drug delivery and regenerative medicine applications, yet inadequate consideration of their compatibility with terminal sterilization presents notable barriers to clinical translation. This Review discusses industry-established terminal sterilization methods and aseptic processing and contrasts them with innovative approaches aimed at preserving the integrity of polymeric implants. Regulatory guidelines, fiscal considerations, and potential pitfalls are discussed to encourage early integration of sterility regulatory considerations in material designs.

Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600.

In the original publication [...].

Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.

While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interactions that can happen in real world diverse patient populations. With a growing abundance of real-world evidence databases containing hundreds of thousands of patient records, it is now feasible to build machine learning models that incorporate individual patient information to provide personalized adverse event predictions. In this study, we build models that integrate patient specific demographic, clinical, and genetic features (when available) with drug structure to predict adverse drug reactions. We develop an extensible graph convolutional approach to be able to integrate molecular effects from the variable number of medications a typical patient may be taking. Our model outperforms standard machine learning methods at the tasks of predicting hospitalization and death in the UK Biobank dataset yielding an R2 of 0.37 and an AUC of 0.90, respectively. We believe our model has potential for evaluating new therapeutic compounds for individualized toxicities in real world diverse populations. It can also be used to prioritize medications when there are multiple options being considered for treatment.