Blood glucose related adverse drug reaction of antitumor monoclonal antibodies: a retrospective analysis using Vigibase

Long-Acting Strategies for Antibody Drugs: Structural Modification, Controlling Release, and Changing the Administration Route

Because of their high specificity, high affinity, and targeting, antibody drugs have been widely used in the treatment of many diseases and have become the most favored new drugs for research in the world. However, some antibody drugs (such as small-molecule antibody fragments) have a short half-life and need to be administered frequently, and are often associated with injection-site reactions and local toxicities during use. Increasing attention has been paid to the development of antibody drugs that are long-acting and have fewer side effects. This paper reviews existing strategies to achieve long-acting antibody drugs, including modification of the drug structure, the application of drug delivery systems, and changing their administration route. Among these, microspheres have been studied extensively regarding their excellent tolerance at the injection site, controllable loading and release of drugs, and good material safety. Subcutaneous injection is favored by most patients because it can be quickly self-administered. Subcutaneous injection of microspheres is expected to become the focus of developing long-lasting antibody drug strategies in the near future.

Uncovering Knowledge Gaps in the Safety Profile of Antiangiogenic Drugs in Cancer Patients: Insights from Spontaneous Reporting Systems Studies

Global repositories of postmarketing safety reports improve understanding of real-life drug toxicities, often not observed in clinical trials. The aim of this scoping review was to map the evidence from spontaneous reporting systems studies (SRSs) of antiangiogenic drugs (AADs) in cancer patients and highlight if the found disproportionality signals of adverse events (AEs) were validated and thus mentioned in the respective Summary of product Characteristics (SmPC). This scoping review was conducted according to PRISMA guidelines for scoping reviews. A knowledge gap on the safety of AADs was found: firstly, several cardiovascular AEs were not mentioned in the SmPCs and no pharmacovigilance studies were conducted despite the well-known safety concerns about these drugs on the cardiovascular system. Second, a disproportionality signal (not validated through causality assessment) of pericardial disease was found in the literature for axitinib with no mention in SmPC of the drug. Despite the exclusion of pharmacoepidemiological studies, we believe that this scoping review, which focuses on an entire class of drugs, could be considered as a novel approach to highlight possible safety concerns of drugs and as a guide for the conduction of a target postmarketing surveillance on AADs.

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

Objective

Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors.

Methods

In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software.

Results

The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r² in curve regressions (nu = 0.5649 × e^{(− (case/6984))}, gamma = 9.005 × 10⁻⁴ × case − 4.877 × 10⁻⁸ × case²). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC).

Conclusion

We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.