Multi-gene Pharmacogenomic Testing That Includes Decision-Support Tools to Guide Medication Selection for Major Depression: A Health Technology Assessment.
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ABSTRACT:
SUBMITTER: Ontario Health (Quality)
PROVIDER: S-EPMC8382305 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Publications
Multi-gene Pharmacogenomic Testing That Includes Decision-Support Tools to Guide Medication Selection for Major Depression: A Health Technology Assessment.
Ontario health technology assessment series 20210812 13
<h4>Background</h4>Major depression is a substantial public health concern that can affect personal relationships, reduce people's ability to go to school or work, and lead to social isolation. Multi-gene pharmacogenomic testing that includes decision-support tools can help predict which depression medications and dosages are most likely to result in a strong response to treatment or to have the lowest risk of adverse events on the basis of people's genes.We conducted a health technology assessm ...[more]
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Project description:BackgroundTreatment in bipolar disorder (BD) is commonly applied as a multimodal therapy based on decision algorithms that lack an integrative understanding of molecular mechanisms or a biomarker associated clinical outcome measure. Pharmacogenetics/genomics study the individual genetic variation associated with drug response. This selective review of pharmacogenomics and pharmacogenomic testing (PGT) in BD will focus on candidate genes and genome wide association studies of pharmacokinetic drug metabolism and pharmacodynamic drug response/adverse event, and the potential role of decision support tools that incorporate multiple genotype/phenotype drug recommendations.Main bodyWe searched PubMed from January 2013 to May 2019, to identify studies reporting on BD and pharmacogenetics, pharmacogenomics and PGT. Studies were selected considering their contribution to the field. We summarize our findings in: targeted candidate genes of pharmacokinetic and pharmacodynamic pathways, genome-wide association studies and, PGT platforms, related to BD treatment. This field has grown from studies of metabolizing enzymes (i.e., pharmacokinetics) and drug transporters (i.e., pharmacodynamics), to untargeted investigations across the entire genome with the potential to merge genomic data with additional biological information.ConclusionsThe complexity of BD genetics and, the heterogeneity in BD drug-related phenotypes, are important considerations for the design and interpretation of BD PGT. The clinical applicability of PGT in psychiatry is in its infancy and is far from reaching the robust impact it has in other medical disciplines. Nonetheless, promising findings are discovered with increasing frequency with remarkable relevance in neuroscience, pharmacology and biology.
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Project description:The objective of this study was to evaluate the potential benefit of utilizing a pharmacogenomic testing report to guide the selection and dosing of psychotropic medications in an outpatient psychiatric practice. The non-randomized, open label, prospective cohort study was conducted from September 2009 to July 2010. In the first cohort, depressed patients were treated without the benefit of pharmacogenomic testing (the unguided group). A DNA sample was obtained from patients in the unguided group, but the results were not shared with either the physicians or patients until the end of the 8-week study period. In the second cohort (the guided group), testing results were provided at the beginning of the 8-week treatment period. Depression ratings were collected at baseline and after 2 weeks, 4 weeks and 8 weeks of treatment using the Quick Inventory of Depressive Symptomatology, Clinician Rated (QIDS-C16) and the 17-item Hamilton Rating Scale for Depression (HAM-D17). Clinician and patient satisfaction was also assessed. The reduction in depressive symptoms achieved within the guided treatment group was greater than the reduction of depressive symptoms in the unguided treatment group using either the QIDS-C16 (P=0.002) or HAM-D17 (P=0.04). We concluded that a rapidly available pharmacogenomic interpretive report provided clinical guidance that was associated with improved clinical outcomes for depressed patients treated in an outpatient psychiatric clinic setting.
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Project description:The effectiveness of selective serotonin reuptake inhibitors (SSRIs) in patients with major depressive disorder (MDD) is controversial.The clinical outcomes of subjects with nonpsychotic MDD were reported and compared with the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study outcomes to provide guidance on the effectiveness of SSRIs.Subjects were treated with citalopram/escitalopram for up to 8 weeks. Depression was measured using the Quick Inventory of Depressive Symptomatology-Clinician Rated (QIDS-C16) and the 17-item Hamilton Depression Rating Scale.The group of subjects with at least 1 follow-up visit had a remission (QIDS-C16 ? 5) rate of 45.8% as well as a response (50% reduction in QIDS-C16) rate of 64.8%, and 79.9% achieved an improvement of 5 points or higher in QIDS-C16 score. The Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study subjects were more likely to achieve a response than STAR*D study subjects. After adjustment for demographic factors, the response rates were not significantly different. When reporting the adverse effect burden, 60.5% of the subjects reported no impairment, 31.7% reported a minimal-to-mild impairment, and 7.8% reported a moderate-to-severe burden at the 4-week visit.Patients contemplating initiating an SSRI to treat their MDD can anticipate a high probability of symptom improvement (79.9%) with a low probability that their symptoms will become worse. Patients with lower baseline severity have a higher probability of achieving remission. The Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study replicates many findings of the first phase of the STAR*D study after controlling for the differences between the studies.
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| S-EPMC7317865 | biostudies-literature
Project description:Pharmacogenomic (PGx) testing is gaining recognition from physicians, pharmacists and patients as a tool for evidence-based medication management. However, seemingly similar PGx testing panels (and PGx-based decision support tools) can diverge in their technological specifications, as well as the genetic factors that determine test specificity and sensitivity, and hence offer different values for users. Reluctance to embrace PGx testing is often the result of unfamiliarity with PGx technology, a lack of knowledge about the availability of curated guidelines/evidence for drug dosing recommendations, and an absence of wide-spread institutional implementation efforts and educational support. Demystifying an often confusing and variable PGx marketplace can lead to greater acceptance of PGx as a standard-of-care practice that improves drug outcomes and provides a lifetime value for patients. Here, we highlight the key underlying factors of a PGx test that should be considered, and discuss the current progress of PGx implementation.
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Project description:BACKGROUND:Decisional tools have demonstrated their importance in informing manufacturing and commercial decisions in the monoclonal antibody domain. Recent approved therapies in regenerative medicine have shown great clinical benefits to patients. OBJECTIVE:The objective of this review was to investigate what decisional tools are available and what issues and gaps have been raised for their use in regenerative medicine. METHODS:We systematically searched MEDLINE to identify articles on decision support tools relevant to tissue engineering, and cell and gene therapy, with the aim of identifying gaps for future decisional tool development. We included published studies in English including a description of decisional tools in regenerative medicines. We extracted data using a predesigned Excel table and assessed the data both quantitatively and qualitatively. RESULTS:We identified 9 articles addressing key decisions in manufacturing and product development challenges in cell therapies. The decision objectives, parameters, assumptions, and solution methods were analyzed in detail. We found that all decisional tools focused on cell therapies, and 6 of the 9 reviews focused on allogeneic cell therapy products. We identified no available tools on tissue-engineering and gene therapy products. These studies addressed key decisions in manufacturing and product development challenges in cell therapies, such as choice of technology, through modeling. CONCLUSIONS:Our review identified a limited number of decisional tools. While the monoclonal antibodies and biologics decisional tool domain has been well developed and has shown great importance in driving more cost-effective manufacturing processes and better investment decisions, there is a lot to be learned in the regenerative medicine domain. There is ample space for expansion, especially with regard to autologous cell therapies, tissue engineering, and gene therapies. To consider the problem more comprehensively, the full needle-to-needle process should be modeled and evaluated.
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Project description:Electronic prescribing is increasingly used, in part because of government incentives for its use. Many of its benefits come from clinical decision support (CDS), but often too many alerts are displayed, resulting in alert fatigue.To characterize the override rates for medication-related CDS alerts in the outpatient setting, the reasons cited for overrides at the time of prescribing, and the appropriateness of overrides.We measured CDS alert override rates and the coded reasons for overrides cited by providers at the time of prescribing. Our primary outcome was the rate of CDS alert overrides; our secondary outcomes were the rate of overrides by alert type, reasons cited for overrides at the time of prescribing, and override appropriateness for a subset of 600 alert overrides. Through detailed chart reviews of alert override cases, and selective literature review, we developed appropriateness criteria for each alert type, which were modified iteratively as necessary until consensus was reached on all criteria.We reviewed 157,483 CDS alerts (7.9% alert rate) on 2,004,069 medication orders during the study period. 82,889 (52.6%) of alerts were overridden. The most common alerts were duplicate drug (33.1%), patient allergy (16.8%), and drug-drug interactions (15.8%). The most likely alerts to be overridden were formulary substitutions (85.0%), age-based recommendations (79.0%), renal recommendations (78.0%), and patient allergies (77.4%). An average of 53% of overrides were classified as appropriate, and rates of appropriateness varied by alert type (p<0.0001) from 12% for renal recommendations to 92% for patient allergies.About half of CDS alerts were overridden by providers and about half of the overrides were classified as appropriate, but the likelihood of overriding an alert varied widely by alert type. Refinement of these alerts has the potential to improve the relevance of alerts and reduce alert fatigue.
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| S-EPMC7654089 | biostudies-literature