10 research outputs found

    Drug Abuse Monitoring: Which Pharmacoepidemiological Resources at the European Level?

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    Monitoring the potential for abuse and dependence of psychoactive substances falls within the scope of international conventions on narcotic drugs. At the European level, this monitoring is based on activities controlled by the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) for substance abuse in general and by the European Medicines Agency (EMA) for marketed drugs, in the context of pharmacovigilance. If France has set up in the early 1990s an original system to assess potential for abuse of psychoactive substances, with specific tools combining both the evaluation of the use of these substances (illicit substances or diverted drugs), and the consequences of that use in terms of morbidity and mortality, there is no equivalent in other European countries. Indeed, unlike the USA, who, for several decades, organized this type of surveillance, with a multisource approach (sentinel systems, databases, medical and administrative data, databases for seeking care in relation abuse), we have not found in other European countries integrated system for identifying a signal of drug abuse, or to assess the impact of measures for minimizing the risk of abuse. However, some recent examples show a growing concern about drug addiction, based on a pharmacoepidemiological approach using pharmacovigilance databases or medical administrative data. These examples illustrate the interest of these approaches in the field of drug of abuse

    Système de surveillance en addictovigilance : quelles données pharmacoépidémiologiques à l’échelle de l’Europe ?

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    La surveillance du potentiel d’abus et de dépendance des substances psychoactives s’articule à l’échelle européenne sur les activités de l’Observatoire Européen des Drogues et Toxicomanies (European Monitoring Centre for Drugs and Drug Addiction [EMCDDA]) et de l’Agence Européenne du Médicament (EMA) pour les médicaments commercialisés. Alors que les États-Unis ont organisé depuis plusieurs décennies un système de surveillance intégré multisources (systèmes sentinelles, bases de données médico-administratives, bases de données de recours aux soins en relation avec l’abus), il n’existe pas d’équivalent dans les autres pays européens, à l’exception de la France, qui a mis en place dès le début des années 1990 un système original d’évaluation de la dépendance, avec des outils pharmacoépidémiologiques spécifiques combinant à la fois l’évaluation de l’utilisation de substances d’abus, médicamenteuses ou non, et des conséquences de cette utilisation en terme de morbi-mortalité. Néanmoins, quelques exemples récents montrent une préoccupation croissante concernant l’addiction médicamenteuse, utilisant dans une approche pharmacoépidémiologique les données de pharmacovigilance ou des bases de données médico-administratives. Ces exemples illustrent tout l’intérêt de ces approches dans ce domaine

    Central and peripheral determinants of fatigue in acute hypoxia

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    This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University on 24th March 2011.Fatigue is defined as an exercise-induced decrease in maximal voluntary force produced by a muscle. Fatigue may arise from central and/or peripheral mechanisms. Supraspinal fatigue (a component of central fatigue) is defined as a suboptimal output from the motor cortex and measured using transcranial magnetic stimulation (TMS). Reductions in O2 supply (hypoxia) exacerbate fatigue and as the severity of hypoxia increases, central mechanisms of fatigue are thought to contribute more to exercise intolerance. In study 1, the feasibility of TMS to measure cortical voluntary activation and supraspinal fatigue of human knee-extensors was determined. TMS produced reliable measurements of cortical voluntary activation within- and between-days, and enabled the assessment of supraspinal fatigue. In study 2, the mechanisms of fatigue during single-limb exercise in normoxia (arterial O2 saturation [SaO2] ~98%), and mild to severe hypoxia (SaO2 93-80%) were determined. Hypoxia did not alter neuromuscular function or cortical voluntary activation of the knee-extensors at rest, despite large reductions in cerebral oxygenation. Maximal force declined by ~30% after single-limb exercise in all conditions, despite reduced exercise time in severe-hypoxia compared to normoxia (15.9 ± 5.4 vs. 24.7 ± 5.5 min; p < 0.05). Peripheral mechanisms of fatigue contributed more to the reduction in force generating capacity of the knee-extensors following single-limb exercise in normoxia and mild- to moderate-hypoxia, whereas supraspinal fatigue played a greater role in severe-hypoxia. In study 3, the effect of constant-load cycling exercise to the limit of tolerance in hypoxia (SaO2 ~80%) and normoxia was investigated. Time to the limit of tolerance was significantly shorter in hypoxia compared to normoxia (3.6 ± 1.3 vs. 8.1 ± 2.9 min; p < 0.001). The reductions in maximal voluntary force and knee-extensor twitch force at task-failure were not different in hypoxia compared to normoxia. However, the level of supraspinal fatigue was exacerbated in hypoxia, and occurred in parallel with reductions in cerebral oxygenation and O2 delivery. Supraspinal fatigue contributes to the decrease in whole-body exercise tolerance in hypoxia, presumably as a consequence of inadequate O2 delivery to the brain

    A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study.

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    BackgroundThe benefit of pharmacogenetic testing before starting drug therapy has been well documented for several single gene-drug combinations. However, the clinical utility of a pre-emptive genotyping strategy using a pharmacogenetic panel has not been rigorously assessed.MethodsWe conducted an open-label, multicentre, controlled, cluster-randomised, crossover implementation study of a 12-gene pharmacogenetic panel in 18 hospitals, nine community health centres, and 28 community pharmacies in seven European countries (Austria, Greece, Italy, the Netherlands, Slovenia, Spain, and the UK). Patients aged 18 years or older receiving a first prescription for a drug clinically recommended in the guidelines of the Dutch Pharmacogenetics Working Group (ie, the index drug) as part of routine care were eligible for inclusion. Exclusion criteria included previous genetic testing for a gene relevant to the index drug, a planned duration of treatment of less than 7 consecutive days, and severe renal or liver insufficiency. All patients gave written informed consent before taking part in the study. Participants were genotyped for 50 germline variants in 12 genes, and those with an actionable variant (ie, a drug-gene interaction test result for which the Dutch Pharmacogenetics Working Group [DPWG] recommended a change to standard-of-care drug treatment) were treated according to DPWG recommendations. Patients in the control group received standard treatment. To prepare clinicians for pre-emptive pharmacogenetic testing, local teams were educated during a site-initiation visit and online educational material was made available. The primary outcome was the occurrence of clinically relevant adverse drug reactions within the 12-week follow-up period. Analyses were irrespective of patient adherence to the DPWG guidelines. The primary analysis was done using a gatekeeping analysis, in which outcomes in people with an actionable drug-gene interaction in the study group versus the control group were compared, and only if the difference was statistically significant was an analysis done that included all of the patients in the study. Outcomes were compared between the study and control groups, both for patients with an actionable drug-gene interaction test result (ie, a result for which the DPWG recommended a change to standard-of-care drug treatment) and for all patients who received at least one dose of index drug. The safety analysis included all participants who received at least one dose of a study drug. This study is registered with ClinicalTrials.gov, NCT03093818 and is closed to new participants.FindingsBetween March 7, 2017, and June 30, 2020, 41 696 patients were assessed for eligibility and 6944 (51·4 % female, 48·6% male; 97·7% self-reported European, Mediterranean, or Middle Eastern ethnicity) were enrolled and assigned to receive genotype-guided drug treatment (n=3342) or standard care (n=3602). 99 patients (52 [1·6%] of the study group and 47 [1·3%] of the control group) withdrew consent after group assignment. 652 participants (367 [11·0%] in the study group and 285 [7·9%] in the control group) were lost to follow-up. In patients with an actionable test result for the index drug (n=1558), a clinically relevant adverse drug reaction occurred in 152 (21·0%) of 725 patients in the study group and 231 (27·7%) of 833 patients in the control group (odds ratio [OR] 0·70 [95% CI 0·54-0·91]; p=0·0075), whereas for all patients, the incidence was 628 (21·5%) of 2923 patients in the study group and 934 (28·6%) of 3270 patients in the control group (OR 0·70 [95% CI 0·61-0·79]; p InterpretationGenotype-guided treatment using a 12-gene pharmacogenetic panel significantly reduced the incidence of clinically relevant adverse drug reactions and was feasible across diverse European health-care system organisations and settings. Large-scale implementation could help to make drug therapy increasingly safe.FundingEuropean Union Horizon 2020

    X-chromosome-wide association study for Alzheimer's disease

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    Due to methodological reasons, the X-chromosome has not been featured in the major genome-wide association studies on Alzheimer's Disease (AD). To address this and better characterize the genetic landscape of AD, we performed an in-depth X-Chromosome-Wide Association Study (XWAS) in 115,841 AD cases or AD proxy cases, including 52,214 clinically-diagnosed AD cases, and 613,671 controls. We considered three approaches to account for the different X-chromosome inactivation (XCI) states in females, i.e. random XCI, skewed XCI, and escape XCI. We did not detect any genome-wide significant signals (P ≤ 5 × 10 ) but identified seven X-chromosome-wide significant loci (P ≤ 1.6 × 10 ). The index variants were common for the Xp22.32, FRMPD4, DMD and Xq25 loci, and rare for the WNK3, PJA1, and DACH2 loci. Overall, this well-powered XWAS found no genetic risk factors for AD on the non-pseudoautosomal region of the X-chromosome, but it identified suggestive signals warranting further investigations. [Abstract copyright: © 2024. The Author(s).

    Multinational cost-utility analysis of panel-based pharmacogenetics-guided treatment of patients enrolled in the U-PGx PREPARE study

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    BackgroundPharmacogenetics (PGx) aims to revolutionize healthcare by individualizing drug doses and medication choices. However, clinical uptake will require positive evaluation evidence of both clinical utility and cost-effectiveness. We have recently demonstrated the clinical utility of this approach, using a panel-based PGx-guided treatment of patients from various indications recruited in seven countries (PREPARE study).MethodsHere, we provide economic evidence from a multinational cost-utility analysis of PGx-guided treatment in 6930 patients participating in the PREPARE study. The study was conducted from March 2017 to June 2020. We used the national healthcare system's perspective in each participating country, including only direct medical costs that budget holders cover. A Visual Analog Scale was used to measure utility and the quality of life was estimated by averaging the Visual Analog Scale scores of participants over four specific time points in the study, namely baseline visit (day 1), week 4, week 12, and 18 months from the baseline visit.FindingsOur analysis showed that the PGx-guided treatment is marginally cost-effective at the threshold of €11,000 QALYs. Cost drivers were hospitalization and ADRs costs, accounting for most of the resources used in both groups (46% and 37.5% in the PGx-guided group versus 49% and 48% in the control group, respectively), as a result of the average duration of hospitalization [1.51 days (95% CI: 1.23-1.82) for the PGx-guided group and 2.37 days (95% CI: 1.95-2.89) for the control group, resulting in a mean difference of 0.86 days (95% CI: 0.37-1.44). The difference in QALYs gained was 0.00178 (95% CI: 0.00176-0.00180). The ICER was €12,020 (95% CI: €10,957-€13,356) per QALY on average (SD: €116). When comparing cost and effectiveness of actionable PGx-guided versus actionable control patients, the total cost for the PGx-guided group was €491 (95% CI: €384-€613), versus €767 (95% CI: €583-€982) in the control group, with an incremental cost difference of €276 (95% CI: €62-€511), favoring the PGx-guided group. Also, the difference in effectiveness was 0.007 QALYs (95% CI: -0.021 to 0.033). Lastly, the difference in the mean total cost was estimated to be €21.4 (95% CI: €19.5-€23.8), while without considering the PGx test cost, indicative of a pre-emptive genetic testing approach, the PGx-guided treatment becomes a cost-saving option, with an estimated savings of approximately €103.6 (€124-€21.4) per patient.InterpretationThese data suggest that panel-based PGx testing is cost-effective, which, together with the clinically beneficial outcomes already demonstrated in the PREPARE study, provides additional evidence of the need to implement PGx into clinical practice.FundingEuropean Union Horizon 2020

    A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study

    No full text
    © 2023Background: The benefit of pharmacogenetic testing before starting drug therapy has been well documented for several single gene–drug combinations. However, the clinical utility of a pre-emptive genotyping strategy using a pharmacogenetic panel has not been rigorously assessed. Methods: We conducted an open-label, multicentre, controlled, cluster-randomised, crossover implementation study of a 12-gene pharmacogenetic panel in 18 hospitals, nine community health centres, and 28 community pharmacies in seven European countries (Austria, Greece, Italy, the Netherlands, Slovenia, Spain, and the UK). Patients aged 18 years or older receiving a first prescription for a drug clinically recommended in the guidelines of the Dutch Pharmacogenetics Working Group (ie, the index drug) as part of routine care were eligible for inclusion. Exclusion criteria included previous genetic testing for a gene relevant to the index drug, a planned duration of treatment of less than 7 consecutive days, and severe renal or liver insufficiency. All patients gave written informed consent before taking part in the study. Participants were genotyped for 50 germline variants in 12 genes, and those with an actionable variant (ie, a drug–gene interaction test result for which the Dutch Pharmacogenetics Working Group [DPWG] recommended a change to standard-of-care drug treatment) were treated according to DPWG recommendations. Patients in the control group received standard treatment. To prepare clinicians for pre-emptive pharmacogenetic testing, local teams were educated during a site-initiation visit and online educational material was made available. The primary outcome was the occurrence of clinically relevant adverse drug reactions within the 12-week follow-up period. Analyses were irrespective of patient adherence to the DPWG guidelines. The primary analysis was done using a gatekeeping analysis, in which outcomes in people with an actionable drug–gene interaction in the study group versus the control group were compared, and only if the difference was statistically significant was an analysis done that included all of the patients in the study. Outcomes were compared between the study and control groups, both for patients with an actionable drug–gene interaction test result (ie, a result for which the DPWG recommended a change to standard-of-care drug treatment) and for all patients who received at least one dose of index drug. The safety analysis included all participants who received at least one dose of a study drug. This study is registered with ClinicalTrials.gov, NCT03093818 and is closed to new participants. Findings: Between March 7, 2017, and June 30, 2020, 41 696 patients were assessed for eligibility and 6944 (51·4 % female, 48·6% male; 97·7% self-reported European, Mediterranean, or Middle Eastern ethnicity) were enrolled and assigned to receive genotype-guided drug treatment (n=3342) or standard care (n=3602). 99 patients (52 [1·6%] of the study group and 47 [1·3%] of the control group) withdrew consent after group assignment. 652 participants (367 [11·0%] in the study group and 285 [7·9%] in the control group) were lost to follow-up. In patients with an actionable test result for the index drug (n=1558), a clinically relevant adverse drug reaction occurred in 152 (21·0%) of 725 patients in the study group and 231 (27·7%) of 833 patients in the control group (odds ratio [OR] 0·70 [95% CI 0·54–0·91]; p=0·0075), whereas for all patients, the incidence was 628 (21·5%) of 2923 patients in the study group and 934 (28·6%) of 3270 patients in the control group (OR 0·70 [95% CI 0·61–0·79]; p <0·0001). Interpretation: Genotype-guided treatment using a 12-gene pharmacogenetic panel significantly reduced the incidence of clinically relevant adverse drug reactions and was feasible across diverse European health-care system organisations and settings. Large-scale implementation could help to make drug therapy increasingly safe. Funding: European Union Horizon 2020
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