WTO amendment on generic drugs draws mixed reaction.
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ABSTRACT:
SUBMITTER: Zarocostas J
PROVIDER: S-EPMC1315635 | biostudies-literature | 2005 Dec
REPOSITORIES: biostudies-literature
Publications
WTO amendment on generic drugs draws mixed reaction.
BMJ (Clinical research ed.) 20051201 7530
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Project description:BackgroundThe extents of generic-reference and generic-generic average bioequivalence and intra-subject variation of on-market drug products have not been prospectively studied on a large scale.MethodsWe assessed bioequivalence of 42 generic products of 14 immediate-release oral drugs with the highest number of generic products on the Saudi market. We conducted 14 four-sequence, randomized, crossover studies on the reference and three randomly-selected generic products of amlodipine, amoxicillin, atenolol, cephalexin, ciprofloxacin, clarithromycin, diclofenac, ibuprofen, fluconazole, metformin, metronidazole, paracetamol, omeprazole, and ranitidine. Geometric mean ratios of maximum concentration (Cmax) and area-under-the-concentration-time-curve, to last measured concentration (AUCT), extrapolated to infinity (AUCI), or truncated to Cmax time of reference product (AUCReftmax) were calculated using non-compartmental method and their 90% confidence intervals (CI) were compared to the 80.00%-125.00% bioequivalence range. Percentages of individual ratios falling outside the ±25% range were also determined.ResultsMean (SD) age and body-mass-index of 700 healthy volunteers (28-80/study) were 32.2 (6.2) years and 24.4 (3.2) kg/m2, respectively. In 42 generic-reference comparisons, 100% of AUCT and AUCI CIs showed bioequivalence, 9.5% of Cmax CIs barely failed to show bioequivalence, and 66.7% of AUCReftmax CIs failed to show bioequivalence/showed bioinequivalence. Adjusting for 6 comparisons, 2.4% of AUCT and AUCI CIs and 21.4% of Cmax CIs failed to show bioequivalence. In 42 generic-generic comparisons, 2.4% of AUCT, AUCI, and Cmax CIs failed to show bioequivalence, and 66.7% of AUCReftmax CIs failed to show bioequivalence/showed bioinequivalence. Adjusting for 6 comparisons, 2.4% of AUCT and AUCI CIs and 14.3% of Cmax CIs failed to show bioequivalence. Average geometric mean ratio deviation from 100% was ≤3.2 and ≤5.4 percentage points for AUCI and Cmax, respectively, in both generic-reference and generic-generic comparisons. Individual generic/reference and generic/generic ratios, respectively, were within the ±25% range in >75% of individuals in 79% and 71% of the 14 drugs for AUCT and 36% and 29% for Cmax.ConclusionsOn-market generic drug products continue to be reference-bioequivalent and are bioequivalent to each other based on AUCT, AUCI, and Cmax but not AUCReftmax. Average deviation of geometric mean ratios and intra-subject variations are similar between reference-generic and generic-generic comparisons.Trial registrationClinicalTrials.gov identifier: NCT01344070 (registered April 3, 2011).
| S-EPMC5721559 | biostudies-literature
Project description:Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of reliable automated pathway evaluation tools. As a critical metric for evaluating chemical reactions, accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios. Currently, accurately predicting yields of interesting reactions still faces numerous challenges, mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors. To compensate for the limitations of high-throughput yield datasets, we curated a generic reaction yield dataset containing 12 reaction categories and rich reaction condition information. Subsequently, by utilizing 2 pretraining tasks based on chemical reaction masked language modeling and contrastive learning, we proposed a powerful bidirectional encoder representations from transformers (BERT)-based reaction yield predictor named Egret. It achieved comparable or even superior performance to the best previous models on 4 benchmark datasets and established state-of-the-art performance on the newly curated dataset. We found that reaction-condition-based contrastive learning enhances the model's sensitivity to reaction conditions, and Egret is capable of capturing subtle differences between reactions involving identical reactants and products but different reaction conditions. Furthermore, we proposed a new scoring function that incorporated Egret into the evaluation of multistep synthesis routes. Test results showed that yield-incorporated scoring facilitated the prioritization of literature-supported high-yield reaction pathways for target molecules. In addition, through meta-learning strategy, we further improved the reliability of the model's prediction for reaction types with limited data and lower data quality. Our results suggest that Egret holds the potential to become an essential component of the next-generation DASP tools.
| S-EPMC10777739 | biostudies-literature
Project description: Not available
| S-EPMC11871538 | biostudies-literature
Project description:ObjectivesThe purpose of this study was to determine how closely generic modified-release antiepileptic drugs (MR-AEDs) resemble reference (brand) formulations by comparing peak concentrations (Cmax), total absorption (area under the curve [AUC]), time to Cmax (Tmax), intersubject variability, and food effects between generic and reference products.MethodsWe tabulated Cmax and AUC data from the bioequivalence (BE) studies used to support the approvals of generic Food and Drug Administration-approved MR-AEDs. We compared differences in 90% confidence intervals of the generic/reference AUC and Cmax geometric mean ratios, and intersubject variability, Tmax and delivery profiles and food effects.ResultsForty-two MR-AED formulations were studied in 3,175 healthy participants without epilepsy in 97 BE studies. BE ratios for AUC and Cmax were similar between most generic and reference products: AUC ratios varied by >15% in 11.4% of BE studies; Cmax varied by >15% in 25.8% of studies. Tmax was more variable, with >30% difference in 13 studies (usually delayed in the fed compared to fasting BE studies). Generic and reference MR products had similar intersubject variability. Immediate-release AEDs showed less intersubject variability in AUC than did MR-AEDs.ConclusionsMost generic and reference MR-AEDs have similar AUC and Cmax values. Ratios for some products, however, are near acceptance limits and Tmax values may vary. Food effects are common with MR-AED products. High variability in pharmacokinetic values for once-a-day MR-AEDs suggests their major advantage compared to immediate-release AED formulations may be the convenience of less frequent dosing to improve adherence.
| S-EPMC4844239 | biostudies-literature
Project description:Effective synthesis planning powered by deep learning (DL) can significantly accelerate the discovery of new drugs and materials. However, most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions (RCs) for their reaction predictions. Currently, the prediction of RCs with a DL framework is hindered by several factors, including: (a) lack of a standardized dataset for benchmarking, (b) lack of a general prediction model with powerful representation, and (c) lack of interpretability. To address these issues, we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot. Through careful design of the model architecture, pretraining method, and training strategy, Parrot improved the overall top-3 prediction accuracy on catalysis, solvents, and other reagents by as much as 13.44%, compared to the best previous model on a newly curated dataset. Additionally, the mean absolute error of the predicted temperatures was reduced by about 4 °C. Furthermore, Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy. Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs. The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable, generalizable, and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.
| S-EPMC10578430 | biostudies-literature
Project description:This meta-analysis aimed to compare the efficacy and adverse events, either serious or mild/moderate, of all generic versus brand-name cardiovascular medicines. We searched randomized trials in MEDLINE, Scopus, EMBASE, Cochrane Controlled Clinical Trial Register, and ClinicalTrials.gov (last update December 1, 2014). Attempts were made to contact the investigators of all potentially eligible trials. Two investigators independently extracted and analyzed soft (including systolic blood pressure, LDL cholesterol, and others) and hard efficacy outcomes (including major cardiovascular adverse events and death), minor/moderate and serious adverse events. We included 74 randomized trials; 53 reported ≥1 efficacy outcome (overall sample 3051), 32 measured mild/moderate adverse events (n = 2407), and 51 evaluated serious adverse events (n = 2892). We included trials assessing ACE inhibitors (n = 12), anticoagulants (n = 5), antiplatelet agents (n = 17), beta-blockers (n = 11), calcium channel blockers (n = 7); diuretics (n = 13); statins (n = 6); and others (n = 3). For both soft and hard efficacy outcomes, 100 % of the trials showed non-significant differences between generic and brand-name drugs. The aggregate effect size was 0.01 (95 % CI -0.05; 0.08) for soft outcomes; -0.06 (-0.71; 0.59) for hard outcomes. All but two trials showed non-significant differences in mild/moderate adverse events, and aggregate effect size was 0.07 (-0.06; 0.20). Comparable results were observed for each drug class and in each stratified meta-analysis. Overall, 8 serious possibly drug-related adverse events were reported: 5/2074 subjects on generics; 3/2076 subjects on brand-name drugs (OR 1.69; 95 % CI 0.40-7.20). This meta-analysis strengthens the evidence for clinical equivalence between brand-name and generic cardiovascular drugs. Physicians could be reassured about prescribing generic cardiovascular drugs, and health care organization about endorsing their wider use.
| S-EPMC4877434 | biostudies-literature
Project description: Not available
| S-EPMC10951731 | biostudies-literature
Project description:BackgroundGeneric medications cost less than brand-name medications and are similarly effective, but brand-name medications are still prescribed. We evaluated patterns in generic cardiovascular medication fills and estimated the potential cost savings with increased substitution of generic for brand-name medications.MethodsThis was a cross-sectional study of cardiovascular therapies using the Medicare Part D database of prescription medications in 2017. We evaluated drug fill patterns for therapies with available brand-name and generic options. We determined the generic substitution ratio and estimated the potential savings with increased generic substitution at the national, state, and clinician level. We compared states with laws related to mandatory pharmacist generic substitution and patient consent for substitution.ResultsOf ≈$22.9 billion spent on cardiovascular drugs in Medicare Part D prescription programs in 2017, ≈$11.0 billion was spent on medications with both brand-name and generic options. Although only 2.4% of medication fills were for the brand-name choice, they made up 21.2% of total spending. Accounting for estimated brand-name rebates, generic substitution for these medications would save $641 million, including $135 million in costs shouldered by patients. Furthermore, the minority of clinicians with the lowest generic utilization was responsible for a large proportion of the potential cost savings.ConclusionsThere are substantial potential cost savings from substituting brand-name medications with generic medications. These savings would be primarily driven by lower use of brand-name therapies by the minority of clinicians who prescribe them at increased rates.
| S-EPMC9070108 | biostudies-literature
Project description:The Non-Biological Complex Drug (NBCD) Working Group defines an NBCD as "a medicinal product, not being a biological medicine, where the active substance is not a homo-molecular structure, but consists of different (closely related and often nanoparticulate) structures that cannot be isolated and fully quantitated, characterized and/or described by physicochemical analytical means". There are concerns about the potential clinical differences between the follow-on versions and the originator products and within the individual follow-on versions. In the present study, we compare the regulatory requirements for developing generic products of NBCDs in the European Union (EU) and the United States (US). The NBCDs investigated included nanoparticle albumin-bound paclitaxel (nab-paclitaxel) injections, liposomal injections, glatiramer acetate injections, iron carbohydrate complexes, and sevelamer oral dosage forms. The demonstration of pharmaceutical comparability between the generic products and the reference products through comprehensive characterization is emphasized for all product categories investigated. However, the approval pathways and detailed requirements in terms of non-clinical and clinical aspects may differ. The general guidelines in combination with product-specific guidelines are considered effective in conveying regulatory considerations. While regulatory uncertainties still prevail, it is anticipated that through the pilot program established by the European Medicines Agency (EMA) and the FDA, harmonization of the regulatory requirements will be achieved, thereby facilitating the development of follow-on versions of NBCDs.
| S-EPMC10208665 | biostudies-literature
Project description:ImportanceDuring the last decade, increases in drug prices for commonly prescribed dermatologic medications have outpaced the rate of inflation, national health care growth, and reimbursements. Among nondermatologic medications, studies have shown a role for robust generic market competition in reducing drug prices. The association between competition and the costs of topical dermatologic generic drugs has not been evaluated.ObjectiveTo characterize the association between changes in drug price and the number of US Food and Drug Administration (FDA)-approved manufacturers among the most commonly used topical dermatologic generic products.Design, setting, and participantsThis retrospective cost analysis of the most commonly prescribed topical dermatologic generic drugs used cumulative annual claims data from the Medicare Part D Prescriber Public User File to identify 597 dermatologist-prescribed drugs with more than 10 claims. The number of manufacturers and the price per unit were identified from the FDA Orange Book and the National Average Drug Acquisition Cost (NADAC) database, respectively, for 2013 through 2016. Drugs that were nondermatologic, were not topically administered, were missing NADAC data, were lacking a generic formulation, or had fewer than 400 claims were excluded.Main outcomes and measuresPrimary outcomes included per-unit drug price and number of FDA-approved manufacturers. Pricing measures were adjusted for inflation and are reported in 2016 dollars.ResultsThe present analysis included 116 topical dermatologic generic formulations, representing 70.5% of the total Medicare Part D dermatologist-coded claims from 2015. Drug formulations with 1 to 2 manufacturers during the study period sustained a median percentage increase in price of 12.7%, whereas those with more than 6 manufacturers had a median percentage decrease in price of 20.5%. Formulations with 1 to 2 manufacturers had a 20.6%, 19.5%, and 33.2% higher percentage increase in price than those with 3 to 4 manufacturers, 5 to 6 manufacturers, and more than 6 manufacturers, respectively. There was a statistically significant inverse association between the percentage change in drug price and median number of manufacturers (Spearman correlation coefficient, -0.26; P = .005).Conclusions and relevanceThe negative association between the change in drug price and the median number of manufacturers of generic topical dermatologic drugs indicates a role for market competition in controlling the costs of generic drug prices within dermatology. These findings support policies that facilitate robust market competition among topical dermatologic generic drugs produced by a limited number of manufacturers.
| S-EPMC6583314 | biostudies-literature