Are Turkish pharmaceutical pricing strategies an access barrier to oncology medicines for Türkiye?

Objectives: Cancer diagnosis is increasing day by day all over the world. Deaths due to cancer are among the most common causes of death. Access to cancer drugs is a priority of health policies. The aim of this study is to evaluate access to cancer drugs through drug box sales data by modeling population growth, cancer incidence, and Fixed Euro Exchange (FEE) rate parameters used in drug pricing in Türkiye. Methods: Access to cancer drugs was evaluated by drug box sales figures obtained from IQVIA. Box sales data were classified according to diagnosis codes (ICD-10), reference, or generic status. Consumption of cancer drugs was examined over time with panel regression analysis, taking into account variables of population growth, cancer incidence, and the FEE rate in drug pricing in Türkiye. Results: The incidence of cancer in Türkiye was 215.1 in 2010 and 223.1 (per hundred thousand) in 2017. Whereas there was a 127.02% increase in the real euro exchange rate, there was an 89.6% increase in the FEE rate. With the regression approach, there is a negative relationship between the real and fixed exchange rate difference (RFED) and reference and generic drug consumption data. Medicine access is affected depending on diagnosis codes at different levels. Colorectal cancer medicine sales had negative correlations for each variable, namely, exchange rate, population growth, and cancer incidence. On the contrary, there was a positive correlation between non-small-cell lung cancer and relevant variables. Innovative medicine groups such as monoclonal antibodies and protein kinase inhibitor consumption showed a negative correlation. Conclusion: According to our results, pricing strategy may be an access barrier for oncology medicines in Türkiye. It should be reviewing the pricing policy that is beneficial for oncology medicine access in Türkiye.

Interaction of endocannabinoid system and cyclooxygenase metabolites with fatty acid amide hydrolase and cyclooxygenase enzyme activities on contractile responses in rat vas deferens tissue.

The endocannabinoid system and prostaglandins are important modulators in the genitourinary system. This study aimed to investigate the possible interactions between the endocannabinoid system and the cyclooxygenase (COX) pathway on rat vas deferens. For this purpose, the concentration responses of the endocannabinoid anandamide, prostaglandin F2α analog latanoprost, and prostaglandin E1 analog misoprostol on the electrical field stimulation (EFS)-induced contractile responses were obtained. The concentration responses to anandamide were obtained again in the presence of nonselective COX inhibitor flurbiprofen and prostaglandin analogs, while the concentration responses of latanoprost and misoprostol were obtained in the presence of cannabinoid receptor antagonists and fatty acid amide hydrolase (FAAH) enzyme inhibitor URB597. FAAH, COX-1, and COX-2 enzyme levels in vas deferens tissue samples were also determined. The cumulative addition of anandamide was not different from the vehicle; however, the EFS-induced contractile responses were significantly increased with the incubation of latanoprost or flurbiprofen in the prostatic portion. Flurbiprofen and misoprostol decreased FAAH enzyme levels in both portions of the vas deferens, while latanoprost induced the inhibition in the prostatic portion. The cumulative administration of latanoprost and misoprostol significantly enhanced the contractile responses in the prostatic portion. This effect of latanoprost was significantly antagonized by URB597 and AM251. The enhancing effect of misoprostol was antagonized by anandamide, URB597, AM251, and AM630. Anandamide, AM251, AM630, and URB597 decreased enzyme levels of COX-1 and COX-2 in both portions of the vas deferens. These results demonstrate an intricate crosstalk between endocannabinoids and prostaglandins in modulation of the vas deferens contractility.

Profile of psychotropic agents used in autism spectrum disorder according to comorbidities in Turkey: A 4-year evaluation.

It is known that the use of psychotropic pharmaceuticals is common in comorbidities seen in autism spectrum disorder (ASD). We have very limited knowledge about which psychotropic drugs are prescribed when comorbidities are diagnosed in patients with ASD. It is aimed to determine the profile of psychotropic agents in patients diagnosed with ASD associated with comorbidities between the ages of 0-24 in Turkey over 4 years. Data belonging to ASD in Prescription Information System (PIS) was obtained from the 'Turkish Medicines and Medical Devices Agency'. A total of 34 066 prescriptions including 45 624 psychotropic drugs were analyzed. A total of psychotropic drugs prescribed for patients with ASD was 75.4%. The following psychotropic drugs were prescribed for the patients with ASD and its comorbidities; risperidone (28.6%), aripiprazole (13.7%), and valproic acid (11.3%) are the most preferred psychotropics. The percentage of pharmaceuticals containing psychotropic active substances in prescriptions with ASD and its comorbidities is 7.5%. This study is the first research in which psychotropics used in ASD were evaluated over a wide period and nationwide. Antipsychotics were most commonly prescribed with the diagnosis of ASD. In the presence of ASD and its comorbidities, risperidone was most frequently prescribed.

A qualitative study on female executive pharmacists with convergent roles.

BACKGROUND: Although women's participation is relatively high in the pharmacist workforce, women remain underrepresented in executive positions. The rate of executive female pharmacists in diversified pharmaceutical areas, from education and research to politics, is recognized as being disproportionately low. OBJECTIVES: In this study, we aimed to explore female executive pharmacists' roles and identify reasons for their being underrepresented in such executive positions in Turkey. METHODS: Semistructured in-depth interviews were conducted from a feminist standpoint with female executive delegates working in the Grand National Assembly of Turkey, pharmacy chambers, and public pharmacy faculties. A thematic data analysis of transcriptions was conducted using MAXQDA 2020 software and was reported according to Consolidated Criteria for Reporting Qualitative Research. RESULTS: The researchers interviewed 19 participants. Three primary themes emerged: gender roles, being an executive, and being a pharmacist. Eight roles came to the surface: mother, child, wife, pharmacist, manager, homemaker, cook, and planner. Taking the responsibility for an executive position involves a continued and simultaneous performance of all other roles as well. CONCLUSION: In this context, female pharmacists' views on gender roles in relation to motherhood, inequalities, and their dedication to their profession came to the fore. This study can be considered as a starting point for studying the underlying causes of the limited representation of female pharmacists in executive positions.

Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm.

While many approaches exist in the literature to learn low-dimensional representations for data collections in multiple modalities, the generalizability of multi-modal nonlinear embeddings to previously unseen data is a rather overlooked subject. In this work, we first present a theoretical analysis of learning multi-modal nonlinear embeddings in a supervised setting. Our performance bounds indicate that for successful generalization in multi-modal classification and retrieval problems, the regularity of the interpolation functions extending the embedding to the whole data space is as important as the between-class separation and cross-modal alignment criteria. We then propose a multi-modal nonlinear representation learning algorithm that is motivated by these theoretical findings, where the embeddings of the training samples are optimized jointly with the Lipschitz regularity of the interpolators. Experimental comparison to recent multi-modal and single-modal learning algorithms suggests that the proposed method yields promising performance in multi-modal image classification and cross-modal image-text retrieval applications.

Cost Evaluation of Inhaler Therapies Used in Respiratory Diseases: 1998--2015 Period in Turkey.

BACKGROUND: With the rise in life expectancy, the burden of chronic diseases, including obstructive pulmonary diseases, has increased throughout the world. OBJECTIVES: To evaluate the sales trends of inhaler pharmaceuticals. METHODS: The changes in box sales and sales amounts (in Turkish lira) of inhaler pharmaceuticals during the period 1998 to 2015 were examined and sales were projected for the next 3 years. Pharmaceuticals were classified according to form and pharmacological groups. RESULTS: The sales of inhaler pharmaceuticals have increased rapidly since 2008. The fastest increase in consumption has occurred in short-acting ß2 agonist preparations and nebulizer pharmaceuticals. Inhaled corticosteroid and long-acting ß2 agonist combination sales have been the highest since 2002, when these products entered the Turkish market. CONCLUSIONS: The inhaler pharmaceutical market has grown over the years, and this growth will continue in the future. The increased use of short-acting preparations, which should be used as symptom relievers, indicates that treatment management continues to be inadequate.

ADHD Medication Trends in Turkey: 2009-2013.

OBJECTIVE: To investigate the change of ADHD medication prescriptions in Turkey between 2009 and 2013. METHOD: Consumption data of ADHD medications, immediate release (IR) methylphenidate (MPH; Ritalin), OROS MPH (Concerta), and atomoxetine (Strattera) were obtained from IMS Health database for the November 2008 to October 2013 period. Defined daily dose (DDD) of each drug was calculated according to WHO definitions and time-series analysis was conducted. RESULTS: There was a significant seasonal effect for prescription of all drugs. Annual use of ADHD medications increased 2.18 times for all ADHD medications combined. DDDs per 1,000 population per day for all ADHD medications were 0.28 in 2009, 0.41 in 2010, 0.52 in 2011, and 0.59 in 2012. OROS MPH represented almost 75% of all ADHD medication utilization. CONCLUSION: As reported from several other countries, ADHD medication use increased in Turkey. Results suggested that over- and underdiagnosis might be seen at the same time.

Introduction and Utilization of High Priced HCV Medicines across Europe; Implications for the Future.

BACKGROUND: Infection with the Hepatitis C Virus (HCV) is a widespread transmittable disease with a diagnosed prevalence of 2.0%. Fortunately, it is now curable in most patients. Sales of medicines to treat HCV infection grew 2.7% per year between 2004 and 2011, enhanced by the launch of the protease inhibitors (PIs) boceprevir (BCV) and telaprevir (TVR) in addition to ribavirin and pegylated interferon (pegIFN). Costs will continue to rise with new treatments including sofosbuvir, which now include interferon free regimens. OBJECTIVE: Assess the uptake of BCV and TVR across Europe from a health authority perspective to offer future guidance on dealing with new high cost medicines. METHODS: Cross-sectional descriptive study of medicines to treat HCV (pegIFN, ribavirin, BCV and TVR) among European countries from 2008 to 2013. Utilization measured in defined daily doses (DDDs)/1000 patients/quarter (DIQs) and expenditure in Euros/DDD. Health authority activities to influence treatments categorized using the 4E methodology (Education, Engineering, Economics and Enforcement). RESULTS: Similar uptake of BCV and TVR among European countries and regions, ranging from 0.5 DIQ in Denmark, Netherlands and Slovenia to 1.5 DIQ in Tayside and Catalonia in 2013. However, different utilization of the new PIs vs. ribavirin indicates differences in dual vs. triple therapy, which is down to factors including physician preference and genotypes. Reimbursed prices for BCV and TVR were comparable across countries. CONCLUSION: There was reasonable consistency in the utilization of BCV and TVR among European countries in comparison with other high priced medicines. This may reflect the social demand to limit the transmission of HCV. However, the situation is changing with new curative medicines for HCV genotype 1 (GT1) with potentially an appreciable budget impact. These concerns have resulted in different prices across countries, with their impact on budgets and patient outcomes monitored in the future to provide additional guidance.

Geometry-Aware Neighborhood Search for Learning Local Models for Image Superresolution.

Local learning of sparse image models has proved to be very effective to solve inverse problems in many computer vision applications. To learn such models, the data samples are often clustered using the K-means algorithm with the Euclidean distance as a dissimilarity metric. However, the Euclidean distance may not always be a good dissimilarity measure for comparing data samples lying on a manifold. In this paper, we propose two algorithms for determining a local subset of training samples from which a good local model can be computed for reconstructing a given input test sample, where we consider the underlying geometry of the data. The first algorithm, called adaptive geometry-driven nearest neighbor search (AGNN), is an adaptive scheme, which can be seen as an out-of-sample extension of the replicator graph clustering method for local model learning. The second method, called geometry-driven overlapping clusters (GOCs), is a less complex nonadaptive alternative for training subset selection. The proposed AGNN and GOC methods are evaluated in image superresolution and shown to outperform spectral clustering, soft clustering, and geodesic distance-based subset selection in most settings.

Out-of-Sample Generalizations for Supervised Manifold Learning for Classification.

Supervised manifold learning methods for data classification map high-dimensional data samples to a lower dimensional domain in a structure-preserving way while increasing the separation between different classes. Most manifold learning methods compute the embedding only of the initially available data; however, the generalization of the embedding to novel points, i.e., the out-of-sample extension problem, becomes especially important in classification applications. In this paper, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with an iterative process. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

Are new models needed to optimize the utilization of new medicines to sustain healthcare systems?

Medicines have made an appreciable contribution to improving health. However, even high-income countries are struggling to fund new premium-priced medicines. This will grow necessitating the development of new models to optimize their use. The objective is to review case histories among health authorities to improve the utilization and expenditure on new medicines. Subsequently, use these to develop exemplar models and outline their implications. A number of issues and challenges were identified from the case histories. These included the low number of new medicines seen as innovative alongside increasing requested prices for their reimbursement, especially for oncology, orphan diseases, diabetes and HCV. Proposed models center on the three pillars of pre-, peri- and post-launch including critical drug evaluation, as well as multi-criteria models for valuing medicines for orphan diseases alongside potentially capping pharmaceutical expenditure. In conclusion, the proposed models involving all key stakeholder groups are critical for the sustainability of healthcare systems or enhancing universal access. The models should help stimulate debate as well as restore trust between key stakeholder groups.

Learning smooth pattern transformation manifolds.

Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals. To construct a manifold, we build a representative pattern whose transformations accurately fit various input images. We examine two objectives of the manifold-building problem, namely, approximation and classification. For the approximation problem, we propose a greedy method that constructs a representative pattern by selecting analytic atoms from a continuous dictionary manifold. We present a dc optimization scheme that is applicable to a wide range of transformation and dictionary models, and demonstrate its application to the transformation manifolds generated by the rotation, translation, and anisotropic scaling of a reference pattern. Then, we generalize this approach to a setting with multiple transformation manifolds, where each manifold represents a different class of signals. We present an iterative multiple-manifold-building algorithm such that the classification accuracy is promoted in the learning of the representative patterns. The experimental results suggest that the proposed methods yield high accuracy in the approximation and classification of data compared with some reference methods, while the invariance to geometric transformations is achieved because of the transformation manifold model.

Discretization of parametrizable signal manifolds.

Transformation-invariant analysis of signals often requires the computation of the distance from a test pattern to a transformation manifold. In particular, the estimation of the distances between a transformed query signal and several transformation manifolds representing different classes provides essential information for the classification of the signal. In many applications, the computation of the exact distance to the manifold is costly, whereas an efficient practical solution is the approximation of the manifold distance with the aid of a manifold grid. In this paper, we consider a setting with transformation manifolds of known parameterization. We first present an algorithm for the selection of samples from a single manifold that permits to minimize the average error in the manifold distance estimation. Then we propose a method for the joint discretization of multiple manifolds that represent different signal classes, where we optimize the transformation-invariant classification accuracy yielded by the discrete manifold representation. Experimental results show that sampling each manifold individually by minimizing the manifold distance estimation error outperforms baseline sampling solutions with respect to registration and classification accuracy. Performing an additional joint optimization on all samples improves the classification performance further. Moreover, given a fixed total number of samples to be selected from all manifolds, an asymmetric distribution of samples to different manifolds depending on their geometric structures may also increase the classification accuracy in comparison with the equal distribution of samples.

Influence of genetic polymorphisms, smoking, gender and age on CYP1A2 activity in a Turkish population.

AIMS: To study the variation in CYP1A2 activity in relation to smoking, gender, age and CYP1A2 polymorphisms. MATERIALS & METHODS: CYP1A2 activity was determined by plasma paraxanthine:caffeine ratio (17X:137X) 4 h after the intake of a standardized cup of coffee in 146 Turkish healthy volunteers. Seven CYP1A2 polymorphisms (-3860G>A, -3113G>A, -2467del/T, -739T>G, -729C>T, -163C>A and 5347T>C) were analyzed. RESULTS: The 17X:137X ratios were increased in smokers (p < 0.0001) and tended to be higher in men both among nonsmokers (p = 0.051) and smokers (p = 0.064). Age-related differences were observed only among nonsmoking women (p = 0.024). The -163C>A polymorphism correlated with 17X:137X ratios only in smokers (p = 0.006). Furthermore, increased 17X:137X ratios were observed in CYP1A2 haplotype H4 (-3860G, -3113G, -2467del, -739T, -729C, -163A and 5347T) carriers in the overall study population (p = 0.026). Multiple regression analyses including smoking, gender, -163C>A genotype and age revealed a significant influence of smoking (p < 0.0001) and gender (p = 0.002) in the overall study population. However, in nonsmokers only the influence of gender remained significant (p = 0.021), while in smokers the influence of the -163C>A genotype held the statistical significance (p = 0.019). The influence of haplotype H4 remained significant (p = 0.028) in the overall study population in similar analyses. CONCLUSION: Smoking has the strongest impact on CYP1A2 activity, while gender and haplotype H4 showed marginal effects. The influence of the -163C>A polymorphism on CYP1A2 activity in smokers suggests an effect on the inducibility of the enzyme.