Anatomical Therapeutic Chemical Classification Research Articles

Use of Azithromycin Attributable to Acute SARS-CoV-2 Infection.

In the early stages of the COVID-19 pandemic, preliminary results that later proved to be incorrect suggested the possible efficacy of anti-infective drugs such as azithromycin for the treatment of SARS-CoV-2 infection. These preliminary data may have influenced the prescription of azithromycin. However, no individual-level data linking the use of this antibiotic to acute SARS-CoV-2 infection are available. The present analysis aims to fill this gap. A retrospective population-based cohort design was used including patients diagnosed with SARS-CoV-2 infection in the period ranging from February 2020 to February 2022. The data source for antibiotic consumption was the drug database of outpatient prescriptions of Emilia-Romagna Region (Italy). Antibiotics were classified according to the Anatomical Therapeutic Chemical (ATC) classification system. Consumption rates and percentages of azithromycin DDDs (defined daily doses) during the acute phase of the infection were compared with a previous control period and with the post-acute phase. Analyses were stratified by four groups according to the prevalent virus variant at time of diagnosis. Comparing the previous control period with the acute phase of infections, the rates of azithromycin consumption (DDD per 1000 individuals per day) increased from 1.17 to 23.11, from 0.80 to 33.03, from 0.81 to 21.01, and from 1.02 to 9.76, in the pre-Alpha, Alpha, Delta, and Omicron periods, respectively. Similarly, the percentages of individuals receiving azithromycin, and the azithromycin DDDs percentages over total systemic antibiotics DDDs increased in acute phases of infection compared with control periods. The consumption rates and percentages returned to preinfection levels in the post-acute phase. In the study period, 12.9% of the use of azithromycin in the entire adult population of Emilia-Romagna was attributable to acute SARS-CoV-2 infection. Considering the low likelihood of bacterial coinfections, the increased azithromycin consumption in the acute phase of SARS-CoV-2 infection suggests inappropriate prescribing of this antibiotic.

Drug-induced Liver Injury in Latin America: 10-year Experience of the Latin American DILI (LATINDILI) Network

Background and aimsLatin America is a region of great interest for studying the clinical presentation of idiosyncratic drug-induced liver injury (DILI). A comprehensive analysis of patients enrolled into the LATINDILI Network over a decade is presented. MethodsDemographics, clinical presentation, histological findings and outcome of prospectively recruited DILI cases in the LATINDILI Network were analyzed. Suspected culprit drugs were classified according to the Anatomical Therapeutic Chemical classification. Causality was assessed using the Roussel Uclaf Causality Assessment Method (RUCAM) scale. ResultsOverall, 468 idiosyncratic DILI cases were analyzed (62% women, mean age 49 years). Hepatocellular injury predominated (62%), jaundice was present in 60% of patients and 42% were hospitalized. 4.1% of the cases had a fatal outcome, and 24 (12%) patients developed chronic DILI. The most common drug classes were systemic anti-infectives (31%), musculoskeletal agents (12%), antineoplastic and immunomodulating agents (11%), and herbal and dietary supplements (HDS, 9%). Notably, none of the patients with DILI due to antibacterials or immunosuppressants had a fatal outcome. In fact, Hy's law showed to have drug-specific predictive value, with anti-tuberculosis drugs, nimesulide and HDS associated with the worst outcome, whereas DILI caused by amoxicillin-clavulanate, nitrofurantoin and diclofenac that fulfilled Hy's law did not have a fatal outcome. ConclusionFeatures of DILI in Latin America are comparable to other prospective registries. However, the pattern of drugs responsible for DILI differs. An increasing incidence of HDS, with high mortality rate, and likewise nimesulide and nitrofurantoin was noted. Thus, public health policies should raise awareness of the potential adverse effects of these compounds.

Unused, leftover and expired medicine and disposal practices among health sciences faculty students in Burdur, Turkey.

To evaluate the opinions of university-level Health Sciences students about unused, leftover and expired medicine, as well as their disposal practices, and to classify the medicines. The cross-sectional study was conducted from April 1 to May 31, 2023, at the Faculty of Health Sciences, Burdur Mehmet Akif Ersoy University, Turkey, and comprised those studying at the Nursing, Nutrition Dietetics and Physical Therapy and Rehabilitation departments. Data was collected using Google Forms. The Anatomical Therapeutic Chemical classification was used for classifying pharmaceutical active ingredients. Data was analysed using SPSS 24. Of the 373 participants, 272(73%) were females and 101(27%) were males. The overall mean age was 20.8±2.8 years. There were 348(93.3%) subejcts who reported having a total of 845 boxes of leftover and unused medicines in their homes (2.3±1.9 per capita), while 25(6.7%) participants had none. The medicines were stored in the kitchen 261(61.5%) as the storage area, and in the refrigerator 181(40.2%) as the storage unit. The expired medicine was disposed of in the garbage in 328(86.1%) cases. Self-medication was prevalent in 325(87.1%) cases. Anatomical Therapeutic Chemical classification analysis showed that paracetamol, acetylsalicylic acid, paracetamol+caffeine and metamizole sodium was the most common group of leftover and unused medicines 283(81.3%). High prevalence of unused and leftover medicine, disposal of medicine in household garbage, and selfmedication behaviour indicated a serious public health and environmental problem.

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.

To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios. The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.

The Clinical Impact of Hospital-Acquired Adverse Drug Reactions in Older Adults: An Australian Cohort Study

IntroductionHospital-acquired adverse drug reactions (HA-ADRs) are common in older adults. However, there is limited knowledge regarding the association between HA-ADRs and adverse clinical outcomes. ObjectiveTo investigate the incidence and characteristics of HA-ADRs in older adults, and any association with mortality, length of stay, and readmissions. DesignProspective cohort study. Setting and ParticipantsFlinders Medical Centre, a large tertiary referral hospital in Adelaide, South Australia. Older adults admitted under the General Medicine and Acute Care of the Elderly units with no previous diagnosis of dementia. MethodsAll patients had a Multidimensional Prognostic Index (MPI) assessment performed within 3 days of the admission. Data collected included age, gender, estimated glomerular filtration rate (eGFR), length of stay, readmissions, and mortality. HA-ADRs were identified by review of individual discharge summaries. Univariate and multivariate analyses were performed to investigate associations with clinical outcomes including mortality, length of stay, and readmissions. Exploratory analyses were performed for HA-ADR groups based on Medical Dictionary for Regulatory Activities System Organ Class and World Health Organization Anatomical Therapeutic Chemical classifications that accounted for ≥10% of all HA-ADRs. ResultsThere were 737 patients in the cohort with 72 having experienced a HA-ADRs (incidence = 9.8%). Patients with an HA-ADR had increased length of stay and 30-day readmissions compared with those without an HA-ADR. In multivariate analysis, the number of HA-ADRs was associated with in-hospital mortality and length of stay but not post-discharge mortality or readmissions within 30 days. In exploratory analyses, patients with an HA-ADR to antibacterial drugs had significantly higher rates of in-hospital mortality compared with those without these reactions. Conclusions and ImplicationsThe number of HA-ADRs are associated with in-hospital mortality and length of stay in older Australian inpatients. The occurrence of HA-ADRs may be a trigger to offer advice to prescribers to prevent future ADRs to similar agents and proactively manage disease to improve health outcomes.

Antibiotic exposure and colon carcinogenesis: In-depth profiling of a 492-patient cohort, with a focus on antibiotic classifications and subtypes.

e15643 Background: To investigate whether the use of specific antibiotics is associated with an increased risk of colon cancer incidence. Methods: We conducted a retrospective cohort study comprising 492 patients diagnosed with colon cancer between 2012 and 2022, with no prior malignancies and a minimum 5-year follow-up period. We collected demographic data, medical histories, and records of antibiotic exposure, categorizing antibiotics systematically based on the Anatomical Therapeutic Chemical (ATC) classification system. Patient antibiotic histories, including drug names, dosages, durations, and therapeutic indications, were included. Antibiotics were classified as broad-spectrum, narrow-spectrum, or extended-course. We performed univariate and multivariate logistic regression models and used propensity score matching to control for potential confounding variables, such as age, gender, co-morbidities, and concurrent medications. Subgroup analyses were conducted to identify specific antibiotic classes associated with an increased risk of colon cancer. Results: The mean age of the cohort was 65.7 years (SD ± 8.3), with an equal gender distribution. Adenocarcinoma was the predominant histological subtype (429 cases, 87%), with rare subtypes accounting for the remaining cases. Common co-morbidities included hypertension (226 cases, 46%), diabetes mellitus (108 cases, 22%), and hyperlipidemia (88 cases, 18%). The most frequently prescribed antibiotics were fluoroquinolones (158 cases, 32%), followed by cephalosporins (108 cases, 22%), and macrolides (88 cases, 18%). Extended-course antibiotics were administered in 138 cases (28%). Preliminary analysis revealed a statistically significant correlation between the use of extended-course antibiotics and the risk of colon cancer (Odds Ratio = 1.89, 95% Confidence Interval: 1.32-2.71, p < 0.05). No substantial association was observed with broad-spectrum or narrow-spectrum antibiotics. Subgroup analysis identified specific agents within the extended-course antibiotics, such as fluoroquinolones and clindamycin, with an elevated risk profile. Conclusions: This study suggests a potential association between the use of extended-course antibiotics and the development of colon cancer. Further validation through larger-scale, prospective studies is strongly recommended to confirm these findings. Further in-depth mechanistic inquiries, particularly in cellular signaling pathways and microbial interactions within the gut microbiome, are warranted to elucidate the underlying biological mechanisms driving this association.

Assessment of the utilization of real-time prescription benefits for patient cost savings within an outpatient setting

BackgroundThis study evaluates the impact of Real-Time Prescription Benefits (RTPB), a tool integrated into electronic health records (EHRs), on patient out-of-pocket costs in an academic institution. RTPB provides prescribers with alternative, less expensive medications based on insurance plans. The primary measure was cost-savings, defined as the difference between the out-of-pocket cost of the prescribed medication and its alternative. MethodsA retrospective analysis of prescriptions from outpatient clinics in a university-based health system was conducted between May 2020 and July 2021. Prescriptions were analyzed at the 2nd level of the Anatomical Therapeutic Chemical (ATC) classification system. Costs were standardized to a 30-day supply. Standardized cost and total cost per prescription, and overall savings for the top 20 medication classes at the 2nd ATC level were calculated. The overall impact of RTPB was estimated based on selecting the least expensive alternative suggested by RTPB. ResultsThe study found that RTPB information was provided for 22% of prescriptions, with suggested alternatives for 1.26%. Among prescriptions with an alternative selected, the standardized average cost saving was $38.83. The study realized $15,416 in patient total cost savings. If the least expensive RTPB-suggested alternative were chosen for all prescriptions, an estimated $276,386 could have been saved. Psychoanaleptic and psycholeptic medications were the most prescribed with an alternative, with most savings in specialty drugs like anthelmintic and immunostimulant medications. ConclusionThe study highlights the importance of RTPB in reducing patient costs. It reports patient cost-savings with RTPB in prescribing decisions. Future research could explore the impact of RTPB on medication adherence using pharmacy claims data.

How is postoperative pain after hip and knee replacement managed? An analysis of two large hospitals in Australia

BackgroundMultimodal analgesia regimens are recommended for the postoperative period after hip and knee replacement surgeries. However, there are no data on practice patterns for analgesic use in the immediate postoperative period after hip and knee replacements in Australia.ObjectivesTo describe analgesic prescribing patterns in the inpatient postoperative phase for patients undergoing hip and knee replacement.MethodsRetrospective study of electronic medical record data from two major hospitals in Sydney, Australia. We identified analgesic medication prescriptions for all patients aged 18 years and older who underwent hip or knee replacement surgery in 2019. We extracted data on pain medications prescribed while in the ward up until discharge. These were grouped into distinct categories based on the Anatomical Therapeutic Chemical classification. We described the frequency (%) of pain medications used by category and computed the average oral morphine equivalent daily dose (OMEDD) during hospitalisation.ResultsWe identified 1282 surgeries in 1225 patients. Patients had a mean (SD) age of 69 (11.8) years; most (57.1%) were female. Over 99% of patients were prescribed opioid analgesics and paracetamol during their hospital stay. Most patients (61.4%) were managed with paracetamol and opioids only. The most common prescribed opioid was oxycodone (87.3% of patients). Only 19% of patients were prescribed nonsteroidal anti-inflammatories (NSAIDs). The median (IQR) average daily OMEDD was 50.2 mg (30.3–77.9).ConclusionWe identified high use of opioids analgesics as the main strategies for pain control after hip and knee replacement in hospital. Other analgesics were much less frequently used, such as NSAIDs, and always in combination with opioids and paracetamol.

Repurposing of drugs against bacterial infections: A pharmacovigilance-based data mining approach.

The World Health Organization (WHO) has published a list of priority pathogens that urgently require research to develop new antibiotics. The main aim of the current study is to identify potential marketed drugs that can be repurposed against bacterial infections. A pharmacovigilance-based drug repurposing approach was used to identify potential drugs. OpenVigil 2.1 tool was used to query the FDA Adverse Event Reporting System database. The reporting odds ratio (ROR) < 1, ROR95CI upper bound <1, and no. of cases ≥30 were used for filtering and sorting of drugs. Sunburst plot was used to represent drugs in a hierarchical order using the Anatomical Therapeutic Chemical classification. Molecular docking and dynamics were performed using the Maestro and Desmond modules of Schrodinger 2023 software respectively. A total of 40 drugs with different classes were identified based on the pharmacovigilance approach which has antibacterial potential. The molecular docking results have shown energetically favored binding conformation of lisinopril against 3-deoxy-manno-octulosonate cytidylyltransferase, UDP-2,3-diacylglucosamine hydrolase, and penicillin-binding protein 3 (PBP3) of Pseudomonas aeruginosa; olmesartan, atorvastatin against lipoteichoic acids flippase LtaA and rosiglitazone and varenicline against d-alanine ligase of Staphylococcus aureus; valsartan against peptidoglycan deacetylase (SpPgdA) and atorvastatin against CDP-activated ribitol for teichoic acid precursors of Streptococcus pneumoniae. Further, molecular dynamic results have shown the stability of identified drugs in the active site of bacterial targets except lisinopril with PBP3. Lisinopril, olmesartan, atorvastatin, rosiglitazone, varenicline, and valsartan have been identified as potential drugs for repurposing against bacterial infection.

Assessment of disease pattern and drug utilization among neurology intensive care unit patients in a developing country: an observational analysis

ABSTRACT Objective This study aimed to assess the disease pattern and drug utilization among admitted patients in a tertiary-care hospital’s neurology intensive care unit (neuro ICU). Methods A prospective observational cohort study was conducted between August 2022 and January 2023. Patients of any age and gender admitted to the neuro ICU were included, but those who declined to participate were excluded. Demographics, clinical, and medication details were consistently gathered and maintained until discharge. The World Health Organization (WHO)/International Network of Rational Use of Drugs (INRUD) prescribing indicators and the Anatomical Therapeutic Chemical (ATC) classification/Defined Daily Dose (DDD) system were used to evaluate drug use. Results A total of 516 patients were included, predominantly male (65.1%), with an average age of 54.62 ± 15.02 years. The most common diagnosis was stroke [72.3%, comprised of hemorrhagic (46.7%) and ischemic (25.6%)], followed by seizure disorders (6.6%), and central nervous system infections (5.4%). Patients received an average of 7.8 medications, 32.3% prescribed by generic name, 16.0% antibiotics, 74.1% injections, and 100% essential drugs. A (28.5%), C (19.2%), N (17.3%), J (19.2%), B (13.5%), and R (2.3%) were commonly prescribed ATC classes of medications. Number of DDDs was maximum for pantoprazole and furosemide. Based on discharged status, 41.0% were discharged on request, 24.8% against medical advice, 23.8% routine, and 10.2% mortality during hospitalization. Conclusion Our study reveals a high prevalence of hemorrhagic stroke, especially among men, diverging from global ischemic stroke trends. Irregular hypertension treatment is the primary cause, exacerbated by low healthcare knowledge in rural areas, where patients often discharge on request, probably due to poor socio-economic conditions. Urgent public awareness campaigns and further research are needed to address this elevated hemorrhagic stroke incidence.

#1018 Profiles of medication use in dialysis patients globally

Abstract Background and Aims Global patterns of medication use in dialysis have not been defined so far. This project aims to provide a real-world view of medication use in dialysis and to characterize therapeutic classes and types of medications. An anonymized dialysis dataset of a global kidney care network called Apollo Dial DB was used. The most common prescribed medications administered at home by the patients were assessed. Method This is a multi-center, retrospective study using real-life data from adult dialysis patients captured in the first version of the Apollo Dial DB dataset. This dataset contains anonymized dialysis data (longitudinal observation level data) on more than 540,000 unique dialysis patients in 40 countries from Jan 2018-Mar 2021. Home medications were analyzed based on Anatomical Therapeutic Chemical (ATC) codes. Top (most common) 5 ATC codes for home medication are presented, once as 4-digit ATC code classes (e.g., A12A for Calcium) and once as full/exact ATC code (e.g., A12AA04 as 7-digit code for Calcium carbonate). Results In Apollo Dial DB 497,723 patients had 10,538,324 documented home medication entries (on average 21 medication prescriptions per patient outside the dialysis clinic), thereof 114,978 patients had 1,607,168 ATC code entries. Each ATC code entry represents a documented prescription of individual length. All percentages shown below are related to 1,607,168 ATC code entries (not to patients). The top 4-digit ATC code classes of home medication were Calcium (8.60%), Vitamin A and D, including combinations (7.33%), All other Therapeutic Products (6.84%), Antithrombotic Agents (5.89%) and Selective Calcium Channel Blockers with mainly vascular Effects (5.39%) (Fig. 1). Out of the ATC class Calcium, Calcium carbonate (4.89%) and Calcium acetate (3.22%) are listed as number 1 and 3 of the top 5 ‘full’ ATC codes. Alfacalcidol (3.36%) of ‘Vitamin A and D, including combinations’ is also number 2 of the ‘full’ ATC code list. Amlodipine (3.07%), number 4 of the full ATC code list, represents the majority of ‘Selective Calcium Channel Blockers with mainly vascular Effects’. Sevelamer of ‘All other Therapeutic Products’ is listed as number 5 in the ATC code list with 2.91% (Fig. 2). Conclusion The top 5 home ATC classes of medications account for 34% of all home prescriptions in dialysis patients. They largely represent phosphate binders, vitamin A &amp; D, antithrombotics, and antihypertensives/antiarrhythmic medications. Although, outside the dialysis clinic each patient has on average 21 medication prescriptions, a lot of these prescriptions represent the major medication classes. It is worthwhile to mention the presented data are related to prescriptions and not to duration of use, e.g., both a medication prescribed for 6 months and a prescription for 1 week are documented only once, even if time on drug varies. Further research is needed to explore global patterns, including further ATC code groups particularly on a patient level, accounting for duration of use and medication formulations, real-world application of various drugs (e.g., GLP1 drugs, HIF PH inhibitor) and accounting for US data with NDC codes.

Analysis of the availability of bronchodilators and anti-inflammatory drugs for patients with chronic obstructive pulmonary disease

Background. The study of drug availability for patients with chronic obstructive pulmonary disease (COPD) represents one of the priority tasks in the organization of effective counteraction to COPD in the Russian Federation.Objective: to assess drug prices, affordability, and availability for COPD patients.Material and methods. The analysis of drug availability for COPD patients was carried out according to the methodology of the World Health Organization and Health Action International (WHO/HAI). Bronchodilator and anti-inflammatory therapy of originator brands and lowestpriced generics was evaluated. The consumption volume of the studied drugs was also analyzed using the ATC/DDD (Anatomical Therapeutic Chemical classification / defined daily dose) pharmacoepidemiologic methodology over a three-year period (from 2020 to 2022), taking into account their share of total DDD (drug utilization analysis, DU90%).Results. According to the results of DU90% analysis, the most purchased drugs for bronchodilator and anti-inflammatory therapy for all the years under study were drugs from the groups of short-acting bronchodilators (salbutamol, fenoterol, ipratropium bromide + fenoterol) and inhaled glucocorticoids (budesonide, beclomethasone). The obtained data were confirmed by the results of the physical availability study. Thus, the highest percentage of physical availability was for short-acting inhaled beta-2-agonists (SABA) (salbutamol). Analysis of drug affordability also revealed a tendential superiority of SABA and short-acting anticholinergics over baseline bronchodilators and antiinflammatory drugs.Conclusion. Low affordability of the main drugs of baseline therapy contributes to the burden of COPD and necessitates improvement of drug supply mechanisms for individuals, especially those with low material income, which in the long term will significantly reduce the costs of the healthcare system for the treatment of these patients by decreasing the severity and frequency of exacerbations.

Computational drug repositioning with attention walking

Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.

Immune Checkpoint Inhibitor Use During Pregnancy and Outcomes in Pregnant Individuals and Newborns

With the widespread use of immune checkpoint inhibitors (ICIs), concerns about their pregnancy outcomes through maternal exposure have emerged, and clinical comparative data are lacking. To assess the risk of pregnancy-, fetal-, and/or newborn-related adverse outcomes associated with exposure to ICIs compared with exposure to other anticancer agents. In this cohort study, all reports mentioning a pregnancy-related condition and an antineoplastic agent (Anatomical Therapeutic Chemical classification group L01) used for a cancer indication registered in the World Health Organization international pharmacovigilance database VigiBase up to June 26, 2022, were extracted. Anticancer agents, including ICIs, used during pregnancy for a cancer indication. Immune checkpoint inhibitors included blockers of programmed cell death 1 (PD1) or its ligand (PD-L1) or cytotoxic T-lymphocyte-associated protein 4 (CTLA4). The main outcome was the reporting odds ratio (ROR) for maternal, fetal, or newborn complications in patients treated with ICIs vs any other anticancer drug. Adverse events, categorized into 45 individual maternofetal adverse outcomes, were directly mapped to Medical Dictionary for Regulatory Activities preferred terms in VigiBase. A total of 3558 reports (ICI: 91 [2.6%]; other anticancer drugs: 3467 [97.4%]) were included in the analysis. In the ICI group, most reports were from the US (60 [65.9%]), and the mean (SD) patient age was 28.9 (10.2) years; in 24 of 55 reports with data on cancer type (43.6%), patients were treated for melanoma. The molecules involved in the ICI group were anti-PD1 (58 reports [63.7%]), anti-PD1 plus anti-CTLA4 (15 [16.5%]), anti-CTLA4 (13 [14.3%]), anti-PD-L1 (4 [4.4%]), and anti-PD1 plus anti-lymphocyte activation gene 3 (1 [1.1%]). An ICI was used in combination with a non-ICI anticancer agent in 10 participants (11.0%). Compared with other anticancer drugs, none of the 45 adverse outcomes identified were overreported in the group exposed to ICIs. However, preterm birth was significantly overreported for the anti-PD1 plus anti-CTLA4 combination compared with other anticancer drugs (12 of 15 [80.0%] vs 793 of 3452 [23.0%]; ROR, 13.87; 95% CI, 3.90-49.28; P < .001) but not for anti-PD-L1 or anti-CTLA4 monotherapy. Three reports of possibly immune-related maternofetal events were identified: 1 case of maternal antiphospholipid syndrome leading to spontaneous abortion, 1 case of pneumonitis leading to neonatal respiratory distress syndrome and death, and 1 case of transient congenital hypothyroidism. In this cohort study of 91 individuals exposed to ICIs during pregnancy, ICI exposure was not associated with overreporting of specific adverse pregnancy, fetal, and/or newborn outcomes compared with other anticancer treatments. However, due to possible rare immune-related neonatal adverse events, ICI use in pregnant women should be avoided when possible, especially the anti-PD1 plus anti-CTLA4 combination.

Utilization Trends of Phosphodiesterase Type-5 Inhibitors for Erectile Dysfunction Between 2019 and 2023 in Tanzania.

Introduction Erectile dysfunction (ED) profoundly affects millions of people globally, including interfering with mental health and quality of life. Phosphodiesterase type-5 inhibitors (PDE5Is) such as sildenafil are pivotal in ED treatment. This study aimed to examine the utilization patterns ofPDE5Is in Tanzania. Materials and methods In this retrospective longitudinal study, data on sildenafil and other similar PDE5Is imported between 2019 and 2023 were sourced from the Tanzania Medicines and Medical Devices Authority (TMDA). Pre-processing and visualization were performed using Microsoft Power BI Desktop, and further analysis was performed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, New York, United States). Utilization trends were ascertained through curve fitting, Holt's linear trend model, and autoregressive integrated moving average (ARIMA) models. The defined daily doses (DDDS) per 1000 inhabitants (DID) were calculated using the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) Classification System and the DDD methodology endorsed by the WHO Collaborating Centre for Drug Statistics Methodology. Results Between 2019 and 2023, there was a pronounced increase in the importation of approximately 587 consignments of PDE5Is. Employing the Holt model (R-square = 0.843), a substantial increase from 0.220910 DID in 2019 to 0.534272 DID by 2025 was observed and anticipated. The period witnessed sildenafil dominating 75.5% of the total use, with Erecto being the most consumed brand (37.6% of totalDID). Notably, 2022 had the highest surge (27.2% of the total), albeit a slight decline was observed in 2023 (20.5%). This trend was supported by a linear regression model (R-square = 0.889). Conclusion We found increasing annual trends of PDE5Is of utilization. This requires critical oversight and effective policies to ensure appropriate use and risk minimization.

Clinical aspects of reimbursement policies for orphan drugs in Central and Eastern European countries.

Objectives: The aim of this study was to characterize the reimbursement policy for orphan drugs (ODs) in Central and Eastern European (CEE) countries in relation to the availability and impact of clinical evidence, health technology assessment (HTA) procedure, selected economic indicators, and the drug type according to indications. Materials and methods: A list of authorized medicines with orphan designation and information about active substance, Anatomical Therapeutic Chemical (ATC) classification, and therapeutic area was extracted from the web-based register of the European Medicines Agency (EMA). A country-based questionnaire survey was performed between September 2021 and January 2022 in a group of selected experts from nine CEE countries (an invitation was sent to 11 countries). A descriptive and statistical analysis was conducted to determine statistical significance, correlations, between the drug or country characteristic and the positive recommendation or reimbursement of ODs. Results: The proportion of reimbursed orphan drugs differed between countries, ranging from 17.7% in Estonia to 49.6% in Hungary (p < 0.001). The odds that ODs were reimbursed were reduced in countries with a "strong" level of impact of drug safety and efficacy on reimbursement decisions (p=0.018), the presence of other additional specific clinical aspects (e.g., genomic data) considered in the reimbursement decision (p < 0.001) and mandatory (without exception) safety assessments (p=0.004). The probability that ODs were reimbursed was increased in countries with a "moderate" level of impact of drug safety and efficacy on reimbursement decisions (p=0.018), when reimbursement decisions are dependent on the EMA registration status and orphan drug designation (p < 0.001), the presence of the "positive HTA recommendation guarantees reimbursement" policy (p < 0.001), higher GDP per inhabitant (p=0.003), and higher healthcare expenditure (p < 0.001). Conclusion: We found that there are differences among CEE countries in the reimbursement of orphan drugs, and we identified aspects that may influence these differences. Safety, efficacy, and specific clinical aspect issues significantly influenced reimbursement decisions. Antineoplastic and immunomodulating agents drugs were the largest group of ODs and increased the chance of getting a positive recommendation. The higher GDP per inhabitant and healthcare expenditures per inhabitant were positively linked to the chance that an OD receives reimbursement.

Hoarding of insulin and oral antidiabetics in the first phase oftheCOVID-19 pandemic: Regulatory actions taken to avoid shortage.

To report on hoarding of prescribed medicines, with focus on insulins, in the early phase of the COVID-19 pandemic and on regulatory actions taken to avoid shortage. The National Prescribed Drug Register which utilizes the Anatomic Therapeutic Chemical (ATC) Classification System and covers the total Swedish population was used. We calculated the number of packages of insulins (ATC code A10A), oral anti-diabetics (A10B), and all medicines across all ATC codes combined (A-S) dispensed per week in 2019 and 2020. Correspondingly, the number of packages of glucose test strips dispensed was calculated using the data source Concise held by the Swedish eHealth Agency. Prompt increases in numbers of dispensed packages were observed in March, peaking at week 11/2020. The absolute numbers of packages dispensed in week 11/2019 and week 11/2020 were: insulin, 49,694 and 95,767, an increase by +92.7%; oral antidiabetics, 55,478 and 82,684, +47.1%; glucose test strips, 18,119 and 23,476, +29:6%; and all medicines across all ATC codes combined, 1,988,456 and 2,659,421, +33.7%. Voluntary restriction of dispensing and a rapid change to applicable regulation were implemented within 2 weeks. A steep decline occurred, which became more pronounced after temporary regulation came in force from April 1, then leveling out during the following months. A signal of insulin hoarding was detected early in the COVID-19 pandemic. A temporary regulation, reducing dispensing to a maximum supply of 3 months was rapidly implemented. A shortage of vitally important prescribed medicines was avoided.

Categorical Analysis of Database Consistency in Reporting Drug-Drug Interactions for Cardiovascular Diseases.

Drug-drug interactions (DDIs) can either enhance or diminish the positive or negative effects of the associated drugs. Multiple drug combinations create difficulties in identifying clinically relevant drug interactions; this is why electronic drug interaction checkers frequently report DDI results inconsistently. Our paper aims to analyze drug interactions in cardiovascular diseases by selecting drugs from pharmacotherapeutic subcategories of interest according to Level 2 of the Anatomical Therapeutic Chemical (ATC) classification system. We checked DDIs between 9316 pairs of cardiovascular drugs and 25,893 pairs of cardiovascular and other drugs. We then evaluated the overall agreement on DDI severity results between two electronic drug interaction checkers. Thus, we obtained a fair agreement for the DDIs between drugs in the cardiovascular category, as well as for the DDIs between drugs in the cardiovascular and other (i.e., non-cardiovascular) categories, as reflected by the Fleiss' kappa coefficients of κ=0.3363 and κ=0.3572, respectively. The categorical analysis of agreement between ATC-defined subcategories reveals Fleiss' kappa coefficients that indicate levels of agreement varying from poor agreement (κ<0) to perfect agreement (κ=1). The main drawback of the overall agreement assessment is that it includes DDIs between drugs in the same subcategory, a situation of therapeutic duplication seldom encountered in clinical practice. Our main conclusion is that the categorical analysis of the agreement on DDI is more insightful than the overall approach, as it allows a more thorough investigation of the disparities between DDI databases and better exposes the factors that influence the different responses of electronic drug interaction checkers. Using categorical analysis avoids potential inaccuracies caused by particularizing the results of an overall statistical analysis in a heterogeneous dataset.

Injectable antibiotic use in India: public-private share in volume and cost.

Consumption of injectable antibiotics is not widely studied, despite injectables constitute a major share of antibiotic cost. This study aimed to understand the share of oral and injectable antibiotic consumption and cost at the national level in India, and the public and private sector shares in the provision and cost of injectables in Kerala state. We used the PharmaTrac private sector sales dataset and the Kerala Medical Services Corporation public sector procurement dataset. Using WHO Access, Watch, Reserve (AWaRe) and Anatomical Therapeutic Chemical (ATC) Classifications, we estimated the annual total and per-capita consumption, and the annual total, per defined daily dose (DDD), and per-capita spending on injectables. Although 94.9% of total antibiotics consumed at the national level were oral preparations, 35.8% of total spending were on injectables. In Kerala , around 33% of total antibiotic spending in the private sector were for injectables, compared to around 25% in the public sector. The public sector used fewer injectable antibiotic formulations (n=21) compared the private sector (n=69). The cost per DDD was significantly higher in the private sector as compared to the public sector. Despite only accounting for 6.3% of the cost share, the public sector provided 31.4% of injectables, indicating very high efficiency. Across both sectors, Watch group antibiotics were significantly more consumed and at a significantly higher cost than Access group antibiotics, for example in nearly double the quantity and at 1.75 times the price per DDD in the private sector. Reserve group antibiotics made up the lowest consumption share (0.61% in the private sector), but at the highest cost per DDD (over 16 times that of Access). Public sector showed higher cost efficiency in antibiotic provisioning compared to private sector. Appropriate antibiotic use cannot be achieved through drug price control alone but requires extensive engagement with private providers through structured stewardship programs.

Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions

In today’s evolving global world, the pharmaceutical sector faces an emerging challenge, which is the rapid surge of the global population and the consequent growth in drug production demands. Recognizing this, our study explores the urgent need to strengthen pharmaceutical production capacities, ensuring drugs are allocated and stored strategically to meet diverse regional and demographic needs. Summarizing our key findings, our research focuses on the promising area of drug demand forecasting using artificial intelligence (AI) and machine learning (ML) techniques to enhance predictions in the pharmaceutical field. Supplied with a rich dataset from Kaggle spanning 600,000 sales records from a singular pharmacy, our study embarks on a thorough exploration of univariate time series analysis. Here, we pair conventional analytical tools such as ARIMA with advanced methodologies like LSTM neural networks, all with a singular vision: refining the precision of our sales. Venturing deeper, our data underwent categorisation and were segmented into eight clusters premised on the ATC Anatomical Therapeutic Chemical (ATC) Classification System framework. This segmentation unravels the evident influence of seasonality on drug sales. The analysis not only highlights the effectiveness of machine learning models but also illuminates the remarkable success of XGBoost. This algorithm outperformed traditional models, achieving the lowest MAPE values: 17.89% for M01AB (anti-inflammatory and antirheumatic products, non-steroids, acetic acid derivatives, and related substances), 16.92% for M01AE (anti-inflammatory and antirheumatic products, non-steroids, and propionic acid derivatives), 17.98% for N02BA (analgesics, antipyretics, and anilides), and 16.05% for N02BE (analgesics, antipyretics, pyrazolones, and anilides). XGBoost further demonstrated exceptional precision with the lowest MSE scores: 28.8 for M01AB, 1518.56 for N02BE, and 350.84 for N05C (hypnotics and sedatives). Additionally, the Seasonal Naïve model recorded an MSE of 49.19 for M01AE, while the Single Exponential Smoothing model showed an MSE of 7.19 for N05B. These findings underscore the strengths derived from employing a diverse range of approaches within the forecasting series. In summary, our research accentuates the significance of leveraging machine learning techniques to derive valuable insights for pharmaceutical companies. By applying the power of these methods, companies can optimize their production, storage, distribution, and marketing practices.