Influence of eight ABCB1 polymorphisms on antidepressant response in a prospective cohort of treatment-free Russian patients with moderate or severe depression: An explorative psychopharmacological study with naturalistic design.

BACKGROUND: Many antidepressants are substrates of P-glycoprotein, an efflux transporter in the blood-brain-barrier encoded by the ABCB1 gene. Genetic variations might influence the transport rate of antidepressants and hence their pharmacological effects. This study investigates the influence of eight polymorphisms in the ABCB1 gene on antidepressant treatment response. METHOD: 152 patients were included from psychiatric departments of the Mental Health Research Institute in Tomsk. The difference in Hamilton-Depression-Rating-Scale (HAMD-17)-scores between baseline and week two, week two and four, and baseline and week four was used to estimate timing of improvement of depression. Associations between the ABCB1 gene-polymorphisms and reduction in HAMD-17 score were assessed using independent t-test and multiple linear regression. RESULTS: Tricyclic antidepressants were associated with a higher reduction of HAMD-17 score when compared to SSRIs. The SNP rs2235040 A-allele had a significant positive influence on the ΔHAMD-17(0→2W) score but a significant negative influence on the ΔHAMD-17(2→4W) score. The rs4148739 G-allele had a significant negative influence on the ΔHAMD-17(0→2W) score but a significant positive influence on the ΔHAMD-17(2→4W) score. The SNP rs2235015 T-allele is significant negatively related to the ΔHAMD-17(2→4W) score. CONCLUSION: ABCB1 Genetic variations appear to affect speed but not magnitude of antidepressant drug response.

Towards elimination of childhood and adolescent tuberculosis in the Netherlands: an epidemiological time-series analysis of national surveillance data.

BACKGROUND: Tuberculosis (TB) in children and adolescents is a sentinel event for ongoing transmission. In the Netherlands, epidemiological characteristics of childhood and adolescent TB have not been fully evaluated. Therefore, we aimed to assess TB epidemiology within this population to provide guidance for TB elimination. METHODS: A retrospective time-series analysis using national surveillance data from 1993-2018 was performed in children (aged <15 years) and adolescents (aged 15-19 years) with TB. Poisson regression models offset with log-population size were used to estimate notification rates and rate ratios. Trends in notification rates were estimated using average annual percentage changes (AAPC) based on the segmented linear regression analysis. RESULTS: Among 3899 children and adolescents with TB notified during 1993-2018, 2418 (62%) were foreign-born (725 (41.3%) out of 1755 children and 1693 (78.9%) out of 2144 adolescents). The overall notification rate in children was 2.3 per 100 000 person-years, declining steadily during the study period (AAPC -10.9%, 95% CI -12.6--9.1). In adolescents, the overall notification rate was 8.4 per 100 000 person-years, strongly increasing during 1993-2001 and 2012-2018. Compared to Dutch-born children and adolescents, substantially higher notification rates were observed among African-born children and adolescents (116.8 and 316.6 per 100 000 person-years, respectively). Additionally, an increasing trend was observed in African-born adolescents (AAPC 18.5%, 95% CI 11.9-25.5). Among the foreign-born population, those from countries in the horn of Africa contributed most to the TB caseload. CONCLUSION: TB notification rate among children was low and constantly declining across different demographic groups. However, heterogeneities were shown in adolescents, with an increasing trend in the foreign-born, particularly those from Africa.

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.

INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. AREAS COVERED: The label for a medicine may evolve as new information on drug safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications. However, the speed at which these updates are made to these AI/ML recommendation systems may be delayed and could influence the safety of prescribing decisions. This article explores the need to keep AI/ML tools 'in sync' with any label changes. Additionally, challenges relating to medicine availability and geographical suitability are discussed. EXPERT OPINION: These considerations highlight the important role that pharmacoepidemiologists and drug safety professionals must play within the monitoring and use of these tools. Furthermore, these issues highlight the guiding role that regulators need to have in planning and oversight of these tools.

Artificial intelligence or machine learning (AI/ML) based systems that guide the prescription of medications have the potential to vastly improve patient care, but these tools should only provide recommendations that are in line with the label of a medicine. With a constantly evolving medication label, this is likely to be a challenge, and this also has implications for the off-label use of medicines.

Tailoring Type II Diabetes Treatment: Investigating the Effect of 5-HTT Polymorphisms on HbA1c Levels after Metformin Initiation.

Aims: To investigate the effect of serotonin transporter (5-HTT) polymorphisms on change in HbA1c levels six months after metformin initiation in type 2 diabetes patients. Materials and Methods: Participants of PROVALID (PROspective cohort study in patients with type 2 diabetes mellitus for VALidation of biomarkers) within the GIANTT (Groningen Initiative to ANalyse Type 2 Diabetes Treatment) cohort who initiated metformin were genotyped for combined 5-HTTLPR/rs25531 (L∗L∗, L∗S∗, and S∗S∗) and 5-HTT VNTR (STin 2.12, 12/-, and 10/-) polymorphisms, respectively. Multiple linear regression was applied to determine the change in HbA1c level from baseline date to six months across 5-HTTLPR/VNTR genotype groups, adjusted for baseline HbA1c, age, gender, triglyceride level, low-density lipoprotein level, and serum creatinine. Results: 157 participants were included, of which 56.2% were male. The average age was 59.3 ± 9.23 years, and the mean baseline HbA1c was 7.49% ± 1.21%. 5-HTTLPR was characterized in 46 patients as L∗L∗, 70 patients as L∗S∗, and 41 patients as S∗S∗ genotypes. No significant association was found between 5-HTTLPR and 5-HTT VNTR genotypes and change in HbA1c after adjustments. Conclusions: 5-HTT polymorphisms did not affect HbA1c levels six months after the start of metformin. Further long-term studies in large samples would be relevant to determine which polymorphisms can explain the variation in response to metformin treatment.

The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness.

The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the 'true impact' that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen.

Community pharmacist-led interventions for patients with type 2 diabetes in low-income and middle-income countries: A scoping review.

BACKGROUND: Studies assessing community pharmacist-led interventions conducted in high-income countries indicate that community pharmacists are successful in taking opportunities to support diabetes management. It is not yet clear as to what extent this is also true for low-income and middle-income countries. OBJECTIVES: To provide an overview of the types of interventions performed by community pharmacists and available evidence about their effects on patients with type 2 diabetes mellitus in low-income and middle-income countries. METHODS: PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials were searched for (non) randomized controlled, before-and-after, and interrupted time series design studies. There was no restriction on publication language. Interventions to be included had to be delivered by community pharmacists in a primary care or community setting. Study quality was assessed using the National Institute of Health tools, with results analyzed qualitatively, and the review itself was conducted in accordance with guidelines for scoping reviews. RESULTS: Twenty-eight studies were included, representing 4,434 patients (mean age from 47.4 to 59.5 years, 55.4% female) from community pharmacies (16 studies), primary care centers (8 studies) or community setting (4 studies). Four studies were single-component and the remaining represented multi-component interventions. Face-to-face counseling of patients was the most common intervention, often combined with the provision of printed materials, remote consultations, or conducting medication reviews. Generally, studies showed improved outcomes in the intervention group, including clinical, patient-reported and medication safety outcomes. In most studies, at least one domain was judged to be of poor quality, with heterogeneity among studies. CONCLUSIONS: Community pharmacist-led interventions on type 2 diabetes mellitus patients showed various positive effects but the quality of the evidence was poor. Face-to-face counseling of varying intensity, often combined with other strategies and representing a multi-component intervention, was the most common type. Although these findings support the expansion of the community pharmacist's role in diabetes care in low-income and middle-income countries, better quality studies are needed to evaluate the impact of specific interventions.

A New Paradigm to Indicate Antidepressant Treatments.

This article develops the idea that clinical depression can be seen as a typical human response, largely rooted in human culture, to events of loss or times of adversity. Various biological, psychological, and social factors may cause some individuals to have a depressive reaction that is ineffectually limited in time and/or severity. Recovery occurs mainly based on natural resilience mechanisms, which come into play spontaneously, but which are sometimes inhibited or blocked by specific pathological biopsychosocial mechanisms. One of the mechanisms for this could be the influence of the circuits that regulate pleasure and happiness, along the dorsal diencephalic connection (DDC) pathway from the forebrain to the midbrain via the habenula. Therapy works by undermining the biopsychosocial factors that prevent the natural recovery mechanism from working. Treatment should, therefore, be seen as facilitating rather than causing natural recovery. This approach is in line with the high recovery rate after placebo treatments and the positive influence of pharmacological treatments with completely different sites of action. Acceptance of this model means that when studying new treatments for depression, a new paradigm must be applied in which the relative value of antidepressant treatment is specifically weighted in terms of enabling the natural resilience process.

Preliminary Pharmacogenetic Study to Explore Putative Dopaminergic Mechanisms of Antidepressant Action.

BACKGROUND: There is sufficient evidence that interference of dopaminergic neurotransmission contributes to the therapeutic effects of antidepressants in unipolar and bipolar depression. METHODS: Hamilton depression rating scale (HAMD 17) scores of 163 at least moderately ill patients with major depressive disorders were used to establish treatment response. HAMD 17 score status was measured before initiation, after two weeks, and after four weeks of treatment with various antidepressants. The possible association between response and genotype in a total of 14 variants of dopamine neurotransmission-related proteins was investigated. RESULTS: DRD4 rs11246226 CA heterozygous patients were found with a greater improvement of HAMD 17 score when compared to homozygous C patients during 0-2 weeks and 0-4 weeks. Patients with MAOB rs1799836 heterozygous GA and homozygous A also demonstrated improved scores during 2-4 weeks and 0-4 weeks. CONCLUSIONS: The results are preliminary due to the limited population size and the small number of variants. Further research into the involvement of habenular dopamine D4 receptors in the antidepressant response is desirable.

Limited Associations Between 5-HT Receptor Gene Polymorphisms and Treatment Response in Antidepressant Treatment-Free Patients With Depression.

Major depressive disorder has become a prominent cause of disability, as lifetime prevalence has increased to ~15% in the Western world. Pharmacological effects of serotonin (5-hydroxytryptamine, 5-HT) are mediated through 5-hydroxytryptamine receptor (5-HTR) binding. Serotonin regulation of amygdala activity is attained through activation of three 5-HT2 family receptor subtypes, 5-HT2A, 5-HT2B, and 5-HT2C. Specifically, HT2A and the HT2C receptors have similar gross cerebral distribution and function, with higher constitutive activity found in HT2C than in HT2A. We investigated the possible association of 5-HTR gene polymorphisms to specific and non-specific antidepressant treatment responses in treatment-free patients in Siberia. 156 patients, aged between 18-70 years and clinically diagnosed with depressive disorders, were treated with antidepressants for 4 weeks. Patients were genotyped for a subset of 29 SNPs from the following 5-HT Receptor genes: HTR1A, HTR1B, HTR2A, HTR2C, HTR3A, HTR3B and HTR6. Primary outcome was measured by differences in Hamilton Depression Rating Scale (ΔHAM-D 17) scores between baseline/week two, week two/week four and baseline/week four. Univariate linear regression was initially conducted to determine the 5-HTR SNPs to be studied within the multiple linear regression. Multiple linear regression analyses over the three time periods were conducted for ΔHAM-D 17 with independent factors including: age, gender, depression diagnosis, antidepressant treatment and selected 5-HTR SNPs. We found improved ∆HAM-D 17 in patients taking tricyclic antidepressants (0-4 weeks: B = 4.85, p = 0.0002; 0-2 weeks: B = 3.58, p = 0.002) compared to patients taking SSRIs. Over the course of study, significant associations between 5-HT receptors SNPs and antidepressant response were not identified.

Investigating the potential role of BDNF and PRL genotypes on antidepressant response in depression patients: A prospective inception cohort study in treatment-free patients.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) is associated with response to antidepressant drugs in mood and anxiety disorders. Prolactin (PRL) is a pituitary hormone with behavioural effects, acting as a neurotrophic factor within the brain and may be involved in antidepressant response. OBJECTIVES: To investigate the relationship between BDNF and PRL genotypes with antidepressant drug response. METHODS: Prospective inception cohort of 186 Russian treatment-free participants (28 men and 158 women) between 18 and 70 years clinically diagnosed with depressive disorder who initiated antidepressant medication. DNA polymorphisms were genotyped for PRL rs1341239, BDNF rs6265 and rs7124442. Primary outcome was measured by differences in Hamilton Depression Rating Scale (∆HAM-D) scores between baseline/week two, week two/week four, and baseline/week four. Linear regression and independent t-test determined the significance between polymorphisms and ∆HAM-D. RESULTS: Comparisons between genotypes did not reveal any significant differences in scores during the first two weeks of treatment. In the latter two weeks, BDNF rs7124442 homozygous C patients responded significantly worse in comparison to homozygous T patients during this period. Further analysis within women and in post-menopausal women found a similar comparison between alleles. LIMITATIONS: Study lasted four weeks, which may be considered short to associate genuine antidepressant effects. CONCLUSIONS: Patients taking tricylic antidepressants were noted to have a significant improvement in ∆HAM-D compared to patients taking SSRIs. Homozygous C BDNF rs712442 patients were found to respond significantly worse in the last two weeks of treatment.