Current state of biologic pharmacovigilance in the European Union: improvements are needed

Introduction: Pharmacovigilance is essential to monitoring the safety profiles of authorized medicines. Compared with small-molecule drugs, biological drugs are more complex, more susceptible to structural variability due to manufacturing processes, and have the potential to induce immune-related reactions, underscoring the importance of safety monitoring for these products. Although highly similar to reference products, biosimilars are not expected to be structurally identical. For these reasons, proper reporting of potential adverse drug reactions (ADRs) using distinguishable names and batch numbers is essential for accurate tracing of all biological drugs. To address the need for robust pharmacovigilance, the European Parliament and Council of the European Union provided legislation regarding pharmacovigilance of biologics in 2010.

Areas covered: This narrative review examines the current state of pharmacovigilance for biologics in the European Union (EU) and discusses relevant information on pharmacovigilance of biosimilars, the current EU pharmacovigilance system, and areas that could be improved.

Expert opinion: Although steps have been taken to improve pharmacovigilance of biologics in the EU, several enhancements can still be made, including additional training for healthcare professionals on ADR reporting, the use of 2D barcodes that enhance traceability, and an open discussion of potentially missed opportunities in the pharmacovigilance of biosimilars.     

Prognostic and predictive significance of nuclear HIF1α expression in locally advanced HNSCC patients treated with chemoradiation with or without nimotuzumab

Background Anti-EGFR-based therapies have limited success in HNSCC patients. Predictive biomarkers are greatly needed to identify the patients likely to be benefited from these targeted therapies. Here, we present the prognostic and predictive association of biomarkers in HPV-negative locally advanced (LA) HNSCC patients.Methods Treatment-naive tumour tissue samples of 404 patients, a subset of randomised Phase 3 trial comparing cisplatin radiation (CRT) versus nimotuzumab plus cisplatin radiation (NCRT) were analysed to evaluate the expression of HIF1α, EGFR and pEGFR by immunohistochemistry and EGFR gene copy change by FISH. Progression-free survival (PFS), locoregional control (LRC) and overall survival (OS) were estimated by Kaplan–Meier method. Hazard ratios were estimated by Cox proportional hazard models.Results Baseline characteristics of the patients were balanced between two treatment groups (CRT vs NCRT) and were representative of the trial cohort. The median follow-up was of 39.13 months. Low HIF1α was associated with better PFS [HR (95% CI) = 0.62 (0.42–0.93)], LRC [HR (95% CI) = 0.56 (0.37–0.86)] and OS [HR (95% CI) = 0.63 (0.43–0.93)] in the CRT group. Multivariable analysis revealed HIF1α as an independent negative prognostic biomarker. For patients with high HIF1α, NCRT significantly improved the outcomes [PFS:HR (95% CI) = 0.55 (0.37–0.82), LRC:HR (95% CI) = 0.55 (0.36–0.85) and OS:HR (95% CI) = 0.54 (0.36–0.81)] compared to CRT. While in patients with low HIF1α, no difference in the clinical outcomes was observed between treatments. Interaction test suggested a predictive value of HIF1α for OS (P = 0.008).Conclusions High HIF1α expression is a predictor of poor clinical response to CRT in HPV-negative LA-HNSCC patients. These patients with high HIF1α significantly benefited with the addition of nimotuzumab to CRT.Clinical trial registration Registered with the Clinical Trial Registry of India (Trial registration identifier—CTRI/2014/09/004980).Subject terms: Tumour biomarkers, Head and neck cancer, Tumour biomarkers, Head and neck cancer, Predictive markers     

Biomedical microelectromechanical systems (BioMEMS): Revolution in drug delivery and analytical techniques

Advancement in microelectromechanical system has facilitated the microfabrication of polymeric substrates and the development of the novel class of controlled drug delivery devices. These vehicles have specifically tailored three dimensional physical and chemical features which together, provide the capacity to target cell, stimulate unidirectional controlled release of therapeutics and augment permeation across the barriers. Apart from drug delivery devices microfabrication technology’s offer exciting prospects to generate biomimetic gastrointestinal tract models. BioMEMS are capable of analysing biochemical liquid sample like solution of metabolites, macromolecules, proteins, nucleic acid, cells and viruses. This review summarized multidisciplinary application of biomedical microelectromechanical systems in drug delivery and its potential in analytical procedures.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.