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Category Archives: Pharmacogenomics

Thermo Fisher unveils its largest and most ethnically diverse array for pharmacogenomic research – BSA bureau

American supplier Thermo Fisher Scientific has launched the new Axiom PangenomiX Array, its largest and most ethnically diverse array to date, offering optimal genetic coverage for population scale disease studies and pharmacogenomic research.

The PangenomiX Array is currently the only research solution that combines four assays in one test: SNP genotyping, whole genome copy number variant detection, fixed copy number discovery, blood and HLA typing. The high-throughput array is designed to advance disease risk and detection research, population-scale disease research programs, ancestry and wellness testing, drug efficacy testing, and drug development research.

Inclusive of clinically relevant pharmacogenomic markers and pathogenic variants, the PangenomiX Array offers researchers enhanced whole-genome imputation and a high level of diversity for testing different ethnicities to keep pace with the growing understanding of the genome. The array has already been used to analyse nearly half a million ethnically diverse samples at a predominant biobank in the US to advance more inclusive research studies related to the prevention, diagnosis and treatment of disease.

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Pharmacogenomic Testing in Major Depression: Benefits, Cost … – HealthDay

WEDNESDAY, Nov. 22, 2023 (HealthDay News) -- For patients with major depressive disorder, pharmacogenomics testing to guide antidepressant use yields population health gains and reduces health system costs, according to a study published online Nov. 14 in CMAJ, the journal of the Canadian Medical Association.

Shahzad Ghanbarian, Ph.D., from the University of British Columbia in Vancouver, Canada, and colleagues developed a discrete-time microsimulation model of care pathway for major depressive disorder in British Columbia to examine the effectiveness and cost-effectiveness of pharmacogenomic testing from the public payer's perspective. Incremental costs, life-years, and quality-adjusted life-years (QALYs) were estimated for a representative cohort of patients.

The researchers found that pharmacogenomic testing was predicted to save the British Columbia health system $956 million over 20 years ($4,926 per patient) and bring health gains of 0.064 and 0.381 life-years and QALYs per patient, respectively, if implemented for adult patients with moderate-to-severe major depressive disorder. The savings were mostly as a result of slowing or preventing the transition to refractory depression. Over 20 years, pharmacogenomic-guided care was associated with 37 percent fewer patients with refractory depression. The costs of pharmacogenomics testing would be offset within about two years of implementation as estimated in sensitivity analyses.

"Interventions that might improve remission rates and reduce the number of cases of refractory depression, in particular, are needed to improve the quality of life for patients, and reduce the economic burden of major depressive disorder on already strained health care systems," the authors write.

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Perceptions of Nigerian medical students regarding their … – BMC Medical Education

A total of 300 medicine and surgery clinical students completed the survey (170 from the University of Lagos and 130 from Lagos State University) resulting in a 40% response rate (calculated as the number of completed questionnaires divided by the potential number of eligible participants based on the MDCN quota for both colleges). The sociodemographic characteristics of the respondents by knowledge, ability and summary scores are shown in Table1. Respondents were 19 to 39 years old with a median age of 23 (IQR: 2224) and slightly higher females (52.3%). At least a quarter of the respondents were from each level, with the majority from sixth (38.3%) and fifth years (36.3%). Most respondents (63.3%) indicated an interest in a career involving research.

Most respondents (92.0%, n=276) indicated they had heard of at least one of the precision medicine terminologies. The most commonly indicated terminology were Pharmacogenomics (71.0%, n=213) and Genomic Medicine (47.7%, n=143), while the least indicated terminologies were Genome-guided prescribing (19.7%, n=59) and Next Generation Sequencing (18.0%, n=54). Among those who had indicated awareness, the most commonly cited source of knowledge was Lectures (49.6%, n=137), Media (34.4%, n=95) and less commonly Healthcare providers (10.1%, n=28) and Peers (5.1%, n=14).

Knowledge scores of the respondents ranged from 4 to 20, with a median knowledge score of 12 (IQR: 814.5). Respondents were more comfortable about their knowledge of genetic variations predisposing to common diseases (43.3%, n=130) and pharmacogenomics (38.0%, n=114). They were least comfortable about their understanding of basic genomic testing concepts and terminology (29.7%, n=89) and next-generation sequencing (23.3%, n=70). The distribution of responses to knowledge questions is shown in Fig.1.

Distribution of knowledge and ability responses of participants

On univariate analyses, respondents medical school year was significantly associated with their knowledge score (F [2,297]=3.23, p=0.04). Compared to those in their 4th year, students in their 6th year had a 1.54-point lower mean knowledge score (95%CI: -2.83, -0.24; p=0.02) while those in 5th year had a 0.39-point lower mean knowledge score but this was not statistically significant (95%CI: -1.69, 0.92; p=0.56). Students who indicated an interest in a career involving research had a borderline significant 1.03-point higher mean knowledge score compared to those who did not (95%CI: -0.03, 2.08; p=0.06). Age, gender and ethnicity of participants did not show any significant associations with knowledge score of the participants.

After sequentially adjusting for age, gender, and interest in a research career, participants medical school year was significantly associated with knowledge score (F [2, 294]=4.78, p=0.009). Students in their 6th year had a statistically significant 2.16-point lower mean knowledge score than those in their 4th year (95%CI: -3.60, -0.72; p=0.003). After adjusting for age, gender, and interest in a career involving research, each unit increase in medical school year was associated with a statistically significant 1.10-point lower mean knowledge score (F [1,295]=8.97, ptrend = 0.003) [Table2].

The ability scores of the respondents ranged from 4 to 20, with a median score of 11 (IQR: 715). Respondents were more comfortable about their ability to recommend genetic testing options to patients (39.0%, n=117), to a lesser extent, understand genomic test results (30.3%, n=91 and were least comfortable in their ability to make treatment recommendations based on genomic test results (29.3%, n=88) and explain genomic test results to patients (29.3%, n=88). The distribution of responses to ability questions is shown in Fig.1.

On univariate analyses, respondents medical school year was significantly associated with ability scores (F [2,297]=6.26, p=0.002). Compared to students in their 4th year, students in their 5th year had a statistically significant 1.47-point lower mean ability score (95%CI: -2.84, -0.09; p=0. 04) while students in their 6th year had a statistically significant 2.44-point lower mean ability score (95%CI: -3.81, -1.08; p<0.001). In addition, each unit increase in knowledge score was significantly associated with a 0.77-point increase in mean ability score (95%CI: 0.69, 0.86; p<0.001). Age, gender, ethnicity of participants and interest in a career involving research did not show any significant associations.

After multivariate adjustments for age, gender, medical school year, interest in a career involving research and knowledge score, participants knowledge score (: 0.76 95%CI: 0.67, 0.84; p<0.001), and medical school year (F [2,293]=4.67, p=0.01) were independent predictors of ability score. Compared to students in their 4th year, students in their 5th year had a 1.24-point lower mean ability score (95%CI: -2.21, -0.27; p=0.01), and those in their 6th year had a 1.58-point lower mean ability score (95%CI: -2.66, -0.50; p=0.004). After adjusting for age, gender, interest in a career involving research and knowledge score, each unit increase in medical school year was associated with a significant 0.78-point lower mean ability score (F [1,294]=8.06, ptrend = 0.005) [Table3].

The attitude scores of participants ranged from 14 to 40, with a median score of 28 (IQR: 2433). The median score on the openness items was 15 (IQR: 1216). Respondents were more willing to use a patients genetic information to guide decisions in clinical practice (62.0%, n=186), use new types of therapies to help patients (60.0%, n=180), and use genome-guided tools developed by researchers (56.0%, n-168) but were less willing to use genome-guided prescribing in their career when senior physicians were not (41.0%, n=123). The median score on the divergence items was 15 (IQR: 1217). Respondents agreed that research-based genome-guided interventions were clinically useful (79.0%, n=237), were willing to prescribe different medications or doses of drugs (61.0%, n=183), to a lesser extent disagreed that clinicians know how to treat patients based on their genetic information better than researchers (52.0%, n=156), and to a much lesser extent disagreed that clinical experience is more important than using a patients genetic information to make decisions (36.3%, n=109). The distribution of responses to attitude questions is shown in Fig.2.

Distribution of participants responses to attitudes questions

Respondents responses to questions assessing their attitudes towards the adoption of genome-guided prescribing and precision medicine. Section A includes the distribution of responses to openness questions while section B includes the distribution of responses to divergence questions

On univariate analyses, each unit increase in knowledge score of the participants was significantly associated with a 0.14 decrease in mean attitude score (95%CI: -0.26, -0.02; p=0.03). Age, gender, ethnicity, medical school year and interest in a career involving research were not significantly associated with attitude scores. Although the association with knowledge score persisted after adjusting for age and gender, adjusting for medical school year and interest in a career involving research resulted in a trend towards a null association. After maximal adjustment for age, gender, knowledge score, and interest in a research career, students in their 6th year had a significant 1.65-point higher mean attitude score than those in their 4th year (95%CI: 0.75, 3.23; p=0.04). However, medical school year overall was not significantly associated with attitude scores (F [2,293]=2.50, p=0.08). Nevertheless, after maximal adjustment, each unit increase in medical school year was significantly associated with a 0.81-point increase in mean attitude scores (95%CI: 0.02, 1.60; ptrend = 0.04) [Table4]. Likelihood ratio chi-square tests did not reveal any evidence of statistical interaction between knowledge scores and medical school year (X2=2.66, p=0.26).

The distribution of ethical concerns expressed by respondents is shown in Fig.3. More than a quarter of the respondents were worried that genomic information obtained would be misused by government and corporate bodies (35.7%, n=107) and that their application would increase margins between the rich and the poor (34.0%, n=102). A similar proportion were worried that results from tests can affect employability if serious genetic defects are made known to their employers (33.0%, n=99) and that they will lead to insurance discrimination (30.0%, n=90). However, less than a quarter of the respondents felt that precision medicine approaches would lead to ethnic/racial discrimination (12.3%, n=37), and only 8.7% (n=26) of the respondents felt that precision medicine approaches would violate privacy and confidentiality.

Respondents perceptions of ethical concerns and education about Precision Medicine

Most respondents (65.0%, n=195) thought it was important to learn about precision medicine. Only 11.3% (n=34) of the respondents felt that their education had adequately prepared them to practice precision medicine. Only 10.7% (n=32) thought they knew who to ask about genomic testing. Finally, only 10.3% (n=31) of the respondents felt their professors had encouraged the use of precision medicine. The distribution of responses to education items is shown in Fig.3.

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Publication Bias Inflates Efficacy of Alprazolam XR: Study Reveals … – HealthDay

WEDNESDAY, Nov. 22, 2023 (HealthDay News) -- Publication bias inflates the apparent efficacy of alprazolam extended-release, according to a study published online Oct. 19 in Psychological Medicine.

Rosa Y. Ahn-Horst, M.D., M.P.H., from Massachusetts General Hospital in Boston, and Erick H. Turner, M.D., from the Veterans Affairs Portland Health Care System in Oregon, examined publication bias with alprazolam by comparing its efficacy for panic disorder using trial results from the published literature and the U.S. Food and Drug Administration. Data were included from all phase 2/3 efficacy trials of alprazolam extended-release (Xanax XR) for the treatment of panic disorder.

The researchers identified five trials in the FDA review, one of which had positive results (20 percent). Of the four trials without positive results, two were published conveying a positive outcome and two were not published. Therefore, according to the three published trials, 100 percent were positive. Using FDA data, alprazolam's effect size was 0.33 versus 0.47 using published data, representing a 42 percent increase.

"Clinicians are well aware of these safety issues, but there's been essentially no questioning of their effectiveness," Turner said in a statement. "Our study throws some cold water on the efficacy of this drug. It shows it may be less effective than people have assumed."

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Effect of pharmacogenomics testing guiding on clinical outcomes in … – BMC Psychiatry

Trivedi MH. Major Depressive Disorder in Primary Care: Strategies for Identification.J Clin Psychiatry. 2020;81(2).

Roca M, Vives M, Lpez-Navarro E, Garca-Campayo J, Gili M. Cognitive impairments and depression: a critical review. Actas Esp Psiquiatr. 2015;43(5):18793.

PubMed Google Scholar

Roca M, Monzn S, Vives M, Lpez-Navarro E, Garcia-Toro M, Vicens C, et al. Cognitive function after clinical remission in patients with melancholic and non-melancholic depression: a 6 month follow-up study. J Affect Disord. 2015;171:8592.

Article PubMed Google Scholar

Rock PL, Roiser JP, Riedel WJ, Blackwell AD. Cognitive impairment in depression: a systematic review and meta-analysis. Psychol Med. 2014;44(10):202940.

Article CAS PubMed Google Scholar

Keilp JG, Gorlyn M, Russell M, Oquendo MA, Burke AK, Harkavy-Friedman J, et al. Neuropsychological function and suicidal behavior: attention control, memory and executive dysfunction in suicide attempt. Psychol Med. 2013;43(3):53951.

Article CAS PubMed Google Scholar

Pridmore S, Auchincloss S. Preventing suicide: a global imperative. Australasian Psychiatry. 2015;23(1):812.

Article Google Scholar

Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. The Lancet. 2018;391(10128):135766.

Article CAS Google Scholar

Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163(11):190517.

Article PubMed Google Scholar

Ghio L, Gotelli S, Marcenaro M, Amore M, Natta W. Duration of untreated illness and outcomes in unipolar depression: a systematic review and meta-analysis. J Affect Disord. 2014;152154:4551.

Article PubMed Google Scholar

Mrazek DA, Hornberger JC, Altar CA, Degtiar I. A review of the clinical, economic, and societal burden of treatment-resistant depression: 19962013. Psychiatr Serv. 2014;65(8):97787.

Article PubMed Google Scholar

Perlis RH. Pharmacogenomic testing and personalized treatment of depression. Clin Chem. 2014;60(1):539.

Article CAS PubMed Google Scholar

Rosenblat JD, Lee Y, McIntyre RS. The effect of pharmacogenomic testing on response and remission rates in the acute treatment of major depressive disorder: a meta-analysis. J Affect Disord. 2018;241:48491.

Article PubMed Google Scholar

Greden JF, Parikh SV, Rothschild AJ, Thase ME, Dunlop BW, DeBattista C, et al. Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: a large, patient- and rater-blinded, randomized, controlled study. J Psychiatr Res. 2019;111:5967.

Article PubMed Google Scholar

Tiwari AK, Zai CC, Altar CA, Tanner JA, Davies PE, Traxler P, et al. Clinical utility of combinatorial pharmacogenomic testing in depression: a canadian patient- and rater-blinded, randomized, controlled trial. Transl Psychiatry. 2022;12(1):101.

Article CAS PubMed PubMed Central Google Scholar

Oslin DW, Lynch KG, Shih MC, Ingram EP, Wray LO, Chapman SR, et al. Effect of pharmacogenomic testing for drug-gene interactions on medication selection and remission of symptoms in major depressive disorder: the PRIME Care Randomized Clinical Trial. JAMA. 2022;328(2):15161.

Article CAS PubMed PubMed Central Google Scholar

Perlis RH, Dowd D, Fava M, Lencz T, Krause DS. Randomized, controlled, participant- and rater-blind trial of pharmacogenomic test-guided treatment versus treatment as usual for major depressive disorder. Depress Anxiety. 2020;37(9):83441.

Article PubMed Google Scholar

Rosenblat JD, Lee Y, McIntyre RS. Does pharmacogenomic testing improve clinical outcomes for major depressive disorder?: a systematic review of clinical trials and cost-effectiveness studies. J Clin Psychiatry. 2017;78(6):7209.

Article PubMed Google Scholar

Brown L, Vranjkovic O, Li J, Yu K, Al Habbab T, Johnson H, et al. The clinical utility of combinatorial pharmacogenomic testing for patients with depression: a meta-analysis. Pharmacogenomics. 2020;21(8):55969.

Article CAS PubMed Google Scholar

McCarthy MJ, Chen Y, Demodena A, Leckband SG, Fischer E, Golshan S, et al. A prospective study to determine the clinical utility of pharmacogenetic testing of veterans with treatment-resistant depression. J Psychopharmacol (Oxford England). 2021;35(8):9921002.

Article Google Scholar

Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.

Article PubMed PubMed Central Google Scholar

Higgins JP, Altman DG, Gtzsche PC, Jni P, Moher D, Oxman AD, et al. The Cochrane collaborations tool for assessing risk of bias in randomised trials. BMJ. 2011;343:d5928.

Article PubMed PubMed Central Google Scholar

Richardson MGP, Donegan S. Interpretation of subgroup analyses in systematic reviews: a tutorial. Clin Epidemiol Glob Health. 2019;7:1928.

Article Google Scholar

Shan X, Zhao W, Qiu Y, Wu H, Chen J, Fang Y, et al. Preliminary clinical investigation of Combinatorial Pharmacogenomic Testing for the Optimized treatment of Depression: a randomized single-blind study. Front Neurosci. 2019;13:960.

Article PubMed PubMed Central Google Scholar

Bradley P, Shiekh M, Mehra V, Vrbicky K, Layle S, Olson MC, et al. Improved efficacy with targeted pharmacogenetic-guided treatment of patients with depression and anxiety: a randomized clinical trial demonstrating clinical utility. J Psychiatr Res. 2018;96:1007.

Article PubMed Google Scholar

Han C, Wang SM, Bahk WM, Lee SJ, Patkar AA, Masand PS, et al. A pharmacogenomic-based antidepressant treatment for patients with major depressive disorder: results from an 8-week, randomized, single-blinded clinical trial. Clin Psychopharmacol Neurosci. 2018;16(4):46980.

Article CAS PubMed PubMed Central Google Scholar

Winner JG, Carhart JM. A prospective, randomized, double-blind study assessing the clinical impact of integrated pharmacogenomic testing for major depressive disorder. Discov Med. 2013;16(89):21927.

PubMed Google Scholar

Prez V, Salavert A, Espadaler J, Tuson M, Saiz-Ruiz J, Sez-Navarro C, et al. Efficacy of prospective pharmacogenetic testing in the treatment of major depressive disorder: results of a randomized, double-blind clinical trial. BMC Psychiatry. 2017;17(1):250.

Article PubMed PubMed Central Google Scholar

Singh AB. Improved antidepressant remission in Major Depression via a pharmacokinetic pathway Polygene Pharmacogenetic Report. Clin Psychopharmacol Neurosci. 2015;13(2):1506.

Article PubMed PubMed Central Google Scholar

Bousman CA, Arandjelovic K, Mancuso SG, Eyre HA, Dunlop BW. Pharmacogenetic tests and depressive symptom remission: a meta-analysis of randomized controlled trials. Pharmacogenomics. 2019;20(1):3747.

Article CAS PubMed Google Scholar

Stingl J, Viviani R, Polymorphism. CYP2D6 and CYP2C19, members of the cytochrome P450 mixed-function oxidase system, in the metabolism of psychotropic drugs. J Intern Med. 2015;277(2):16777.

Article CAS PubMed Google Scholar

Zeier Z, Carpenter LL, Kalin NH, Rodriguez CI, McDonald WM, Widge AS, et al. Clinical implementation of pharmacogenetic decision support tools for antidepressant drug prescribing. Am J Psychiatry. 2018;175(9):87386.

Article PubMed PubMed Central Google Scholar

Bousman CA, Hopwood M. Commercial pharmacogenetic-based decision-support tools in psychiatry. Lancet Psychiatry. 2016;3(6):58590.

Article PubMed Google Scholar

Hicks JK, Bishop JR, Sangkuhl K, Mller DJ, Ji Y, Leckband SG, et al. Clinical pharmacogenetics implementation Consortium (CPIC) Guideline for CYP2D6 and CYP2C19 genotypes and dosing of selective serotonin reuptake inhibitors. Clin Pharmacol Ther. 2015;98(2):12734.

Article CAS PubMed PubMed Central Google Scholar

Hicks JK, Sangkuhl K, Swen JJ, Ellingrod VL, Mller DJ, Shimoda K, et al. Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update. Clin Pharmacol Ther. 2017;102(1):3744.

Article CAS PubMed Google Scholar

Juki MM, Haslemo T, Molden E, Ingelman-Sundberg M. Impact of CYP2C19 genotype on Escitalopram exposure and therapeutic failure: a retrospective study based on 2,087 patients. Am J Psychiatry. 2018;175(5):46370.

Article PubMed Google Scholar

Sinyor M, Schaffer A, Levitt A. The sequenced treatment alternatives to relieve depression (STAR*D) trial: a review. Can J Psychiatry. 2010;55(3):12635.

Article PubMed Google Scholar

Kato M, Serretti A. Review and meta-analysis of antidepressant pharmacogenetic findings in major depressive disorder. Mol Psychiatry. 2010;15(5):473500.

Article CAS PubMed Google Scholar

Szegedi A, Rujescu D, Tadic A, Mller MJ, Kohnen R, Stassen HH, et al. The catechol-O-methyltransferase Val108/158Met polymorphism affects short-term treatment response to mirtazapine, but not to paroxetine in major depression. Pharmacogenomics J. 2005;5(1):4953.

Article CAS PubMed Google Scholar

Arias B, Serretti A, Lorenzi C, Gast C, Cataln R, Faans L. Analysis of COMT gene (val 158 Met polymorphism) in the clinical response to SSRIs in depressive patients of european origin. J Affect Disord. 2006;90(23):2516.

Article CAS PubMed Google Scholar

Kato M, Fukuda T, Wakeno M, Fukuda K, Okugawa G, Ikenaga Y, et al. Effects of the serotonin type 2A, 3A and 3B receptor and the serotonin transporter genes on paroxetine and fluvoxamine efficacy and adverse drug reactions in depressed japanese patients. Neuropsychobiology. 2006;53(4):18695.

Article CAS PubMed Google Scholar

Desta Z, Zhao X, Shin JG, Flockhart DA. Clinical significance of the cytochrome P450 2C19 genetic polymorphism. Clin Pharmacokinet. 2002;41(12):91358.

Article CAS PubMed Google Scholar

Bertilsson L. Geographical/interracial differences in polymorphic drug oxidation. Current state of knowledge of cytochromes P450 (CYP) 2D6 and 2C19. Clin Pharmacokinet. 1995;29(3):192209.

Article CAS PubMed Google Scholar

Zhou SF. Polymorphism of human cytochrome P450 2D6 and its clinical significance: part II. Clin Pharmacokinet. 2009;48(12):761804.

Article CAS PubMed Google Scholar

Bradford LD. CYP2D6 allele frequency in european Caucasians, Asians, Africans and their descendants. Pharmacogenomics. 2002;3(2):22943.

Article CAS PubMed Google Scholar

Kunugi H, Hattori M, Kato T, Tatsumi M, Sakai T, Sasaki T, et al. Serotonin transporter gene polymorphisms: ethnic difference and possible association with bipolar affective disorder. Mol Psychiatry. 1997;2(6):45762.

Article CAS PubMed Google Scholar

Bousman CA, Bengesser SA, Aitchison KJ, Amare AT, Aschauer H, Baune BT, et al. Review and Consensus on Pharmacogenomic Testing in Psychiatry. Pharmacopsychiatry. 2021;54(1):517.

Article PubMed Google Scholar

Abdullah-Koolmees H, van Keulen AM, Nijenhuis M, Deneer VHM. Pharmacogenetics Guidelines: Overview and Comparison of the DPWG, CPIC, CPNDS, and RNPGx Guidelines.Frontiers in Pharmacology. 2020;11.

Benitez J, Cool CL, Scotti DJ. Use of combinatorial pharmacogenomic guidance in treating psychiatric disorders. Per Med. 2018;15(6):48194.

Article CAS PubMed Google Scholar

Perlis RH, Mehta R, Edwards AM, Tiwari A, Imbens GW. Pharmacogenetic testing among patients with mood and anxiety disorders is associated with decreased utilization and cost: a propensity-score matched study. Depress Anxiety. 2018;35(10):94652.

Article CAS PubMed Google Scholar

Brown LC, Lorenz RA, Li J, Dechairo BM. Economic utility: combinatorial pharmacogenomics and medication cost savings for Mental Health Care in a primary care setting. Clin Ther. 2017;39(3):592602e1.

Article PubMed Google Scholar

Bousman CA, Forbes M, Jayaram M, Eyre H, Reynolds CF, Berk M, et al. Antidepressant prescribing in the precision medicine era: a prescribers primer on pharmacogenetic tools. BMC Psychiatry. 2017;17(1):60.

Article PubMed PubMed Central Google Scholar

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Who are the leading innovators in microbiota restoration therapy for … – Pharmaceutical Technology

The pharmaceutical industry continues to be a hotbed of innovation, with activity driven by the evolution of new treatment paradigms, and the gravity of unmet needs, as well as the growing importance of technologies such as pharmacogenomics, digital therapeutics, and artificial intelligence. In the last three years alone, there have been over 633,000 patents filed and granted in the pharmaceutical industry, according to GlobalDatas report on Immuno-oncology in Pharmaceuticals: Microbiota restoration therapy.

According to GlobalDatas Technology Foresights, which uses over 756,000 patents to analyse innovation intensity for the pharmaceutical industry, there are 110 innovation areas that will shape the future of the industry.

Microbiota restoration therapy is a key innovation area in immuno-oncology

Microbiota restoration therapy can be composed of human faecal material containing viable gut flora from a patient or donor, and include a diluent and a cryoprotectant. The human faecal material is screened before using it in the restoration therapy for any pathogenic microorganisms.

GlobalDatas analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 240+ companies, spanning technology vendors, established pharmaceutical companies, and up-and-coming start-ups engaged in the development and application of microbiota restoration therapy.

Key players in microbiota restoration therapy a disruptive innovation in the pharmaceutical industry

Application diversity measures the number of different applications identified for each relevant patent and broadly splits companies into either niche or diversified innovators.

Geographic reach refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from global to local.

Source: GlobalData Patent Analytics

Probiotical is the leading patent filer for microbiota restoration therapy. Probiotical is a manufacturer of probiotics and synbiotics products. The companys activities consist of several stages of research and development, strain isolation, characterisation and production of probiotic strains for the prevention and treatment of various diseases, and design and implementation of specific probiotics and synbiotics finished products in many therapeutic areas, supported by clinical studies.

In terms of application diversity, Fate Therapeutics is the top company, followed by Imstem Biotechnology and the Spanish National Research Council. By means of geographic reach, the Spanish National Research Council holds the top position. While GI Innovation and Vitacare stand in second and third positions, respectively.

To further understand the key themes and technologies disrupting the pharmaceutical industry, access GlobalDatas latest thematic research report on Pharmaceutical.

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalDatas Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the worlds largest industries.

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