Pharmacokinetics and Generic Drug Switching: A Regulator's View

Contrastive analysis on the safety of brand and generic nebivolol: a real-world pharmacovigilance study based on the FDA adverse event reporting system

Background: The equivalence of generic drugs to their brand-name counterparts is a controversial issue. Current literature indicates disparities between the generic nebivolol (GN) and the brand nebivolol (BN). Aim: The study is designed to investigate the safety difference between GN and BN and provide reference information for clinical practice. Methods: We reviewed adverse event (AE) reports that recorded nebivolol as the primary suspect drug in the FDA Adverse Event Reporting System (FAERS) database from 2004 to 2022, conducted a disproportional analysis to detect signals for the GN and BN respectively, and compared the AE heterogeneity between them using the Breslow-Day test. Results: A total of 2613 AE reports of nebivolol were recorded in the FAERS database from 2004 to 2022, of which 2,200 were classified as BN, 346 as GN, and 67 unclassifiable AE reports were excluded. The signals of 37 AEs distributed in cardiac, gastrointestinal, psychiatric, and nervous systems were detected in disproportional analysis. 33 out of 37 AEs were positive signals, with 21 not previously listed on the drug label, indicating an unrecognized risk with nebivolol. In the heterogeneity analysis of AE signals between GN and BN, the GN generally showed a higher AE signal value than BN, especially 15 AEs distributed in the cardiac, neurological, and psychiatric systems that showed statistically significantly higher risk by taking GN. Conclusion: Our study shows some previously overlooked adverse effects of nebivolol. It suggests that the risk of GN's adverse effects may be higher than those in BN, which deserves further attention and investigation by healthcare professionals, regulators, and others.

Revisiting the in-vitro and in-vivo considerations for in-silico modelling of complex injectable drug products

Complex injectable drug products (CIDPs) have often been developed to modulate the pharmacokinetics along with efficacy for therapeutic agents used for remediation of chronic disorders. The effective development of CIDPs has exhibited complex kinetics associated with multiphasic drug release from the prepared formulations. Consequently, predictability of pharmacokinetic modelling for such CIDPs has been difficult and there is need for advanced complex computational models for the establishment of accurate prediction models for in-vitro-in-vivo correlation (IVIVC). The computational modelling aims at supplementing the existing knowledge with mathematical equations to develop formulation strategies for generation of predictable and discriminatory IVIVC. Such an approach would help in reduction of the burden of effect of hidden factors on preclinical to clinical translations. Computational tools like physiologically based pharmacokinetics (PBPK) modelling have combined physicochemical and physiological properties along with IVIVC characteristics of clinically used formulations. Such techniques have helped in prediction and understanding of variability in pharmacodynamic parameters of potential generic products to clinically used formulations like Doxil®, Ambisome®, Abraxane® in healthy and diseased population using mathematical equations. The current review highlights the important formulation characteristics, in-vitro, preclinical in-vivo aspects which need to be considered while developing a stimulatory predictive PBPK model in establishment of an IVIVC and in-vitro-in-vivo relationship (IVIVR).

A collaborative study of the impact of N-nitrosamines presence and ARB recall on ARB utilization - results from IQVIA™ Disease Analyzer Germany

PURPOSE

Regulators are increasingly concerned with  the impact of recalls on drug adherence. In 2018, N-nitrosamines impurities were detected in valsartan containing medical products. Concerned products were immediately recalled in July 2018 by regulatory agencies internationally. In Germany, recalls were issued for valsartan, losartan and irbesartan from July 2018 to March 2019. This study examined angiotensin II receptor blocker (ARB) utilization trends and switching patterns in Germany before and after July 2018.

METHODS

Patients prescribed ARBs from January 2014 to June 2020 in general practices in Germany were included in a collaborative framework common protocol drug utilization study led by the US Food and Drug Administration. Trends in monthly and quarterly proportions of total ARB prescribing were analysed for individual ARBs using descriptive statistics and interrupted time series analysis. The rate of switching to an alternative ARB was analysed before and after the recalls.

RESULTS

The proportion of valsartan prescriptions immediately decreased from 35.9 to 17.8% following the first recalls in July 2018, mirrored by an increased proportion for candesartan. Increased switching from valsartan to candesartan was observed. No increased switching was observed after losartan recalls, whereas for irbesartan, increased switching was observed 6-12 months after the last recall. Increased switching from ARBs to angiotensin-converting enzyme (ACE) inhibitors or ARB treatment discontinuations were not observed.

CONCLUSION

This study showed that patients were able to continue ARB treatment despite the July 2018-March 2019 recalls, although many patients needed to switch to an alternative ARB. The duration of the impact of ARB recalls appeared to be limited.

Network Theory and Switching Behaviors: A User Guide for Analyzing Electronic Records Databases

As part of studies that employ health electronic records databases, this paper advocates the employment of graph theory for investigating drug-switching behaviors. Unlike the shared approach in this field (comparing groups that have switched with control groups), network theory can provide information about actual switching behavior patterns. After a brief and simple introduction to fundamental concepts of network theory, here we present (i) a Python script to obtain an adjacency matrix from a records database and (ii) an illustrative example of the application of network theory basic concepts to investigate drug-switching behaviors. Further potentialities of network theory (weighted matrices and the use of clustering algorithms), along with the generalization of these methods to other kinds of switching behaviors beyond drug switching, are discussed.

Bioequivalence and switchability of generic antiseizure medications (ASMs): A re-appraisal based on analysis of generic ASM products approved in Europe

The safety of switching between generic products of antiseizure medications (ASMs) continues to be a hot topic in epilepsy management. The main reason for concern relates to the uncertainty on whether, and when, two generics found to be bioequivalent to the same brand (reference) product are bioequivalent to each other, and the risk of a switch between generics resulting in clinically significant changes in plasma ASM concentrations. This article addresses these concerns by discussing the distinction between bioequivalence and statistical testing for significant difference, the importance of intra-subject variability in interpreting bioequivalence studies, the stricter regulatory bioequivalence requirements applicable to narrow-therapeutic-index (NTI) drugs, and the extent by which currently available generic products of ASMs comply with such criteria. Data for 117 oral generic products of second-generation ASMs approved in Europe by the centralized, mutual recognition or decentralized procedure were analyzed based on a review of publicly accessible regulatory assessment reports. The analysis showed that for 99% of generic products assessed (after exclusion of gabapentin products), the 90% confidence intervals (90% CIs) of geometric mean ratios (test/reference) for AUC (area under the drug concentration vs time curve) were narrow and wholly contained within the acceptance interval (90%-111%) applied to NTI drugs. Intra-subject variability for AUC was <10% for 53 (88%) of the 60 products for which this measure was reported. Many gabapentin generics showed broader, 90% CIs for bioequivalence estimates, and greater intra-subject variability, compared with generics of other ASMs. When interpreted within the context of other available data, these results suggest that any risk of non-bioequivalence between these individual generic products is small, and that switches across these products are not likely to result in clinically relevant changes in plasma drug exposure. The potential for variability in exposure when switching across generics is likely to be greatest for gabapentin.