Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models

Does patient reporting lead to earlier detection of drug safety signals? A retrospective comparison of time to reporting between patients and healthcare professionals in a global database:A Retrospective Comparison of Time to Reporting Between Patients and Healthcare Professionals in a Global Database

OBJECTIVE: To explore if there is a difference between patients and healthcare professionals (HCPs) in time to reporting drug-adverse drug reaction (ADR) associations which led to drug safety signals. DESIGN: This was a retrospective comparison of time to reporting selected drug-ADR associations which led to drug safety signals between patients and healthcare professionals. SETTING: ADR reports were selected from the World Health Organization Global database of individual case safety reports, VigiBase. Reports were selected based on drug-ADR associations of actual drug safety signals. MAIN OUTCOME MEASURES: Primary outcome was the difference in time to reporting between patients and HCPs. The date of the first report for each individual signal was used as time zero. The difference in time between the date of the reports and time zero was calculated. Statistical differences in timing were analysed on the corresponding survival curves using a Mann-Whitney U test. RESULTS: In total 2822 reports were included, of which 52.7% were patient reports, with a median of 25% for all included signals. For all signals, median time to signal detection was 10.4 years. Overall, HCPs reported earlier than patients: median 7.0 vs 8.3 years (p <0.001). CONCLUSIONS: Patients contributed a large proportion of reports on drug-ADR pairs that eventually became signals. HCPs reported 1.3 year earlier than patients. These findings strengthen the evidence on the value of patient reporting in signal detection, and highlight an opportunity to encourage patients to report suspected ADRs even earlier in the future

Translational biomedical informatics and pharmacometrics approaches in the drug interactions research

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research

Quantitative methods to support drug benefit-risk assessment

Joint evaluation of drugs’ beneficial and adverse effects is required in many situations, in particular to inform decisions on initial or sustained marketing of drugs, or to guide the treatment of individual patients. This synthesis, known as benefit-risk assessment, is without doubt important: timely decisions supported by transparent and sound assessments can reduce mortality and morbidity in potentially large groups of patients. At the same time, it can be hugely complex: drug effects are generally disparate in nature and likelihood, and the information that needs to be processed is diverse, uncertain, deficient, or even unavailable. Hence there is a clear need for methods that can reliably and efficiently support the benefit-risk assessment process. For already marketed drugs, this process often starts with the detection of previously unknown risks that are subsequently integrated with all other relevant information for joint analysis. In this thesis, quantitative methods are devised to support different aspects of drug benefit-risk assessment, and the practical usefulness of these methods is demonstrated in clinically relevant case studies. Shrinkage regression is adapted and implemented for large-scale screening in collections of individual case reports, leading to the discovery of a link between methylprednisolone and hepatotoxicity. This adverse effect is then considered as part of a complete benefit-risk assessment of methylpredniso­lone in multiple sclerosis relapses, set in a general framework of probabilistic decision analysis. Two methods devised in the thesis substantively contribute to this assessment: one for efficient generation of utility distributions for the considered clinical outcomes, driven by modelling of qualitative information; and one for computing risk limits for rare and otherwise non-quantifiable adverse effects, based on collections of individual case reports.At the time of the doctoral defence the following papers were unpublished and had a status as follows: Paper 6: Manuscript; Paper 7: Manuscript.</p

Quantitative methods to support drug benefit-risk assessment

Joint evaluation of drugs’ beneficial and adverse effects is required in many situations, in particular to inform decisions on initial or sustained marketing of drugs, or to guide the treatment of individual patients. This synthesis, known as benefit-risk assessment, is without doubt important: timely decisions supported by transparent and sound assessments can reduce mortality and morbidity in potentially large groups of patients. At the same time, it can be hugely complex: drug effects are generally disparate in nature and likelihood, and the information that needs to be processed is diverse, uncertain, deficient, or even unavailable. Hence there is a clear need for methods that can reliably and efficiently support the benefit-risk assessment process. For already marketed drugs, this process often starts with the detection of previously unknown risks that are subsequently integrated with all other relevant information for joint analysis. In this thesis, quantitative methods are devised to support different aspects of drug benefit-risk assessment, and the practical usefulness of these methods is demonstrated in clinically relevant case studies. Shrinkage regression is adapted and implemented for large-scale screening in collections of individual case reports, leading to the discovery of a link between methylprednisolone and hepatotoxicity. This adverse effect is then considered as part of a complete benefit-risk assessment of methylpredniso­lone in multiple sclerosis relapses, set in a general framework of probabilistic decision analysis. Two methods devised in the thesis substantively contribute to this assessment: one for efficient generation of utility distributions for the considered clinical outcomes, driven by modelling of qualitative information; and one for computing risk limits for rare and otherwise non-quantifiable adverse effects, based on collections of individual case reports.At the time of the doctoral defence the following papers were unpublished and had a status as follows: Paper 6: Manuscript; Paper 7: Manuscript.</p