Efficacy of colistin alone and in various combinations for the treatment of experimental osteomyelitis due to carbapenemase-producing Klebsiella pneumoniae.

OBJECTIVES: In a new experimental model of carbapenemase-producing Klebsiella pneumoniae osteomyelitis we evaluated the efficacy of colistin alone and in various combinations and examined the emergence of colistin-resistant strains and cross-resistance to host defence peptides (HDPs). METHODS: KPC-99YC is a clinical strain with intermediate susceptibility to meropenem (MIC = 4 mg/L) and full susceptibility to gentamicin, colistin and tigecycline (MICs = 1 mg/L) and fosfomycin (MIC = 32 mg/L). Time-kill curves were performed at 4× MIC. Osteomyelitis was induced in rabbits by tibial injection of 2 × 108 cfu. Treatment started 14 days later for 7 days in seven groups: (i) control; (ii) colistin; (iii) colistin + gentamicin; (iv) colistin + tigecycline; (v) colistin + meropenem; (vi) colistin + meropenem + gentamicin; and (vii) colistin + fosfomycin. RESULTS: In vitro, colistin was rapidly bactericidal, but regrowth occurred after 9 h. Combinations of colistin with meropenem or fosfomycin were synergistic, whereas combination with tigecycline was antagonistic. In vivo, colistin alone was not effective. Combinations of colistin with meropenem or fosfomycin were bactericidal (P < 0.001) and the addition of gentamicin enhanced the efficacy of colistin + meropenem (P = 0.025). Tigecycline reduced the efficacy of colistin (P = 0.007). Colistin-resistant strains emerged in all groups except colistin + fosfomycin and two strains showed cross-resistance to HDP LL-37. CONCLUSIONS: In this model, combinations of colistin plus meropenem, with or without gentamicin, or colistin plus fosfomycin were the only effective therapies. The combination of colistin and tigecycline should be administered with caution, as it may be antagonistic in vitro and in vivo.

The Use of Biomarkers in Pharmacovigilance: A Systematic Review of the Literature.

Background: The use of biomarkers varies from disease etiognosis and diagnosis to signal detection, risk prediction, and management. Biomarker use has expanded in recent years, however, there are limited reviews on the use of biomarkers in pharmacovigilance and specifically in the monitoring and management of adverse drug reactions (ADRs). Objective: The objective of this manuscript is to identify the multiple uses of biomarkers in pharmacovigilance irrespective of the therapeutic area. Design: This is a systematic review of the literature. Data Sources and Methods: Embase and MEDLINE database searches were conducted for literature published between 2010-March 19, 2021. Scientific articles that described the potential use of biomarkers in pharmacovigilance in sufficient detail were reviewed. Papers that did not fulfill the United States Food and Drug Administration (US FDA) definition of a biomarker were excluded, which is based on the International Conference on Harmonisation (ICH)-E16 guidance. Results: Twenty-seven articles were identified for evaluation. Most articles involved predictive biomarkers (41%), followed by safety biomarkers (38%), pharmacodynamic/response biomarkers (14%), and diagnostic biomarkers (7%). Some articles described biomarkers that applied to multiple categories. Conclusion: Various categories of biomarkers including safety, predictive, pharmacodynamic/response, and diagnostic biomarkers are being investigated for potential use in pharmacovigilance. The most frequent potential uses of biomarkers in pharmacovigilance in the literature were the prediction of the severity of an ADR, mortality, response, safety, and toxicity. The safety biomarkers identified were used to evaluate patient safety during dose escalation, identify patients who may benefit from further biomarker testing during treatment, and monitor ADRs.

The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature.

INTRODUCTION: Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. OBJECTIVE: The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. METHODS: Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. RESULTS: Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. CONCLUSION: Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.

Safety surveillance and challenges in accelerated COVID-19 vaccine development.

The COVID-19 pandemic, caused by a novel type of coronavirus, continues to infect people, increasing morbidity and mortality across the globe. Measures to slow the transmission of the virus have had limited impact, and people, businesses, and economies have suffered. The disease has disproportionally impacted elderly and individuals with certain pre-existing conditions and has highlighted health and social inequities in some racial and ethnic minority groups. The majority of those who contract the disease recover completely, but some experience long-lasting complications. Vaccines have the potential to end the pandemic, and through the intense collaboration of scientists in government and private sectors, more than 200 COVID-19 candidate vaccines have been or are being developed, using known platforms and previous experiences with severe acute respiratory syndrome (SARS), at unprecedented speed. The expectations for vaccine safety and quality in the setting of accelerated development are the same as during non-emergency times; however, challenges inherent with the circumstances of the pandemic situation provide opportunities to improve clinical trial conduct and strengthen pharmacovigilance systems. We have reviewed and analyzed existing PV guidelines and recommendations throughout the lifecycle of vaccine development with a focus on developing a global/worldwide effort for post-marketing vaccine safety surveillance. Plain Language Summary: The Important Role of Pharmacovigilance in Accelerated COVID-19 Vaccine Development This is an extensive review that intends to address important aspects of COVID-19 vaccines' accelerated development and safety surveillance. It is focused on regulatory requirements for long-term safety monitoring, practical applications, and current global efforts in developing robust pharmacovigilance systems for post-authorization surveillance.Notably, different perspectives of authors from industry, academic institutions, and contract research organizations involved in drug safety were incorporated to reflect on various regulatory requirements and new developments in vaccine safety. All co-authors are current members of International Society of Pharmacovigilance (ISoP).

Enhancing Pharmacovigilance from the US Experience: Current Practices and Future Opportunities.

This review is intended to present perspectives from the US experience in enhancing pharmacovigilance on current practices and future opportunities. Best practices concepts could be applied worldwide through the presentation of how three pillars of pharmacovigilance: (1) medical and scientific excellence, (2) operational and compliance excellence, and (3) knowledge sharing and experts development in the field could serve as a framework for the establishment of an efficient and successful global pharmacovigilance system.