In vitro dissolution profile comparison using bootstrap bias corrected similarity factor, f2.

In vitro dissolution profile has been shown to be correlated with the drug absorption and has often been considered as a metric for assessing in vitro bioequivalence between a test product and corresponding reference one. Various methods have been developed to assess the similarity between two dissolution profiles. In particular, similarity factor f2 has been reviewed and discussed extensively in many statistical articles. Although the f2 lacks inferential statistical properties, the estimation of f2 and its various modified versions were the most widely used metric for comparing dissolution profiles. In this paper, we investigated performances of the naive f2 estimate method, bootstrap f2 confidence interval method and bias corrected-accelerated (BCa) bootstrap f2 confidence interval method for comparing dissolution profiles. Our studies show that naive f2 estimate method and BCa bootstrap f2 confidence interval method are unable to control the type I error rate. The bootstrap f2 confidence interval method can control the type I error rate under a specific level. However, it will cause great conservatism on the power of the test. To solve the potential issues of the previous methods, we recommended a bootstrap bias corrected (BC) f2 confidence interval method in this paper. The type I error rate, power and sensitivity among different f2 methods were compared based on simulations. The recommended bootstrap BC f2 confidence interval method shows better control of type I error than the naive f2 estimate method and BCa bootstrap f2 confidence interval method. It also provides better power than the bootstrap f2 confidence interval method.

MicroRNA-143-3p ameliorates collagen-induced arthritis by polarizing naive CD4+ T cells into Treg cells.

BACKGROUND: Rheumatoid arthritis (RA) is a persistent and systemic autoimmunity disease. The abnormal differentiation of Treg cells is important in pathogenesis. Despite previous studies showed that microRNAs (miRNAs, miR) are pivotal modulators of Treg cells, the effect of miRNAs on Treg cell differentiation and function is not clear. Our study wants to reveal the relationship of miR-143-3p with the differentiative ability and biofunction of Treg cells during the development of RA. METHODS: The Expressing level of miR-143-3p and cell factor generation in peripheral blood (PB) of RA sufferers were identified by ELISA or RT-qPCR. The roles of miR-143-3p in Treg cell differentiation were studied via ShRNA/lentivirus transfection. Male DBA/1 J mice were separated into control, model, control mimics, and miR-143-3p mimics groups to analyze the anti-arthritis efficacy, the differentiative ability of Treg cells, and the expression level of miR-143-3p. RESULTS: Our team discovered that the Expressing level of miR-143-3p was related to RA disease activities in a negative manner, and remarkably related to antiinflammation cell factor IL-10. In vitro, the expression of miR-143-3p in the CD4+ T cells upregulated the percentage of CD4+ CD25+ Fxop3+ cells (Tregs) and forkhead box protein 3 (Foxp3) mRNA expression. Evidently, miR-143-3p mimic intervention considerably upregulated the content of Treg cells in vivo, validly avoided CIA progression, and remarkably suppressed the inflammatory events of joints in mice. CONCLUSION: Our findings indicated that miR-143-3p could ameliorate CIA through polarizing naive CD4+ T cells into Treg cells, which may be a novel strategy to treat autoimmune diseases such as RA.

Modeling Approaches to Reimagine Stability (MARS) for Enabling Earlier Access to Critical Drugs for Patients with Unmet Medical Needs.

FDA's experience to date has shown that completion of stability data requirements is one of the most observed challenges for applicants of New Drug Applications (NDAs) with an expedited review designation. Since NDAs submitted under these expedited pathways often have limited available real-time stability data from the primary batches, Modeling Approaches to Reimagine Stability (MARS) have been proposed to support establishment of tentative retest periods of the drug substance and/or expiration dating period (shelf-life) of the drug product. MARS incorporate statistical principles and available tools as a part of the predictive models. In this study, a data mining exercise has been conducted with regulatory submissions of Investigational New Drug (IND) Applications, NDAs, and Abbreviated New Drug Applications (ANDAs) containing MARS data. The case studies presented herein demonstrate how MARS data has been applied to regulatory scenarios involving prediction of retest and/or shelf-life, bridging major development changes, and confirming that no degradation has been observed or predicted. Using the assumption of a linear time trend for those cases that do not display sufficient degradation to conduct MARS for projection of an expiration date, an analysis of covariance (ANCOVA) model is developed and described herein to test the hypothesis of zero slope by a p-value method. Our results show that the application of MARS adequately supported establishment of a tentative commercially viable retest date/shelf-life, thus enabling earlier access to critical drugs for patients with unmet medical needs.

Panax notoginseng saponins (PNS) attenuate Th17 cell differentiation in CIA mice via inhibition of nuclear PKM2-mediated STAT3 phosphorylation.

CONTEXT: Rheumatoid arthritis (RA) is an autoimmune disease with aberrant Th17 cell differentiation. Panax notoginseng (Burk.) F. H. Chen (Araliaceae) saponins (PNS) have an anti-inflammatory effect and can suppress Th17 cell differentiation. OBJECTIVE: To investigate mechanisms of PNS on Th17 cell differentiation in RA, and the role of pyruvate kinase M2 (PKM2). MATERIALS AND METHODS: Naive CD4+T cells were treated with IL-6, IL-23 and TGF-ß to induce Th17 cell differentiation. Apart from the Control group, other cells were treated with PNS (5, 10, 20 µg/mL). After the treatment, Th17 cell differentiation, PKM2 expression, and STAT3 phosphorylation were measured via flow cytometry, western blots, or immunofluorescence. PKM2-specific allosteric activator (Tepp-46, 50, 100, 150 µM) and inhibitor (SAICAR, 2, 4, 8 µM) were used to verify the mechanisms. A CIA mouse model was established and divided into control, model, and PNS (100 mg/kg) groups to assess an anti-arthritis effect, Th17 cell differentiation, and PKM2/STAT3 expression. RESULTS: PKM2 expression, dimerization, and nuclear accumulation were upregulated upon Th17 cell differentiation. PNS inhibited the Th17 cells, RORγt expression, IL-17A levels, PKM2 dimerization, and nuclear accumulation and Y705-STAT3 phosphorylation in Th17 cells. Using Tepp-46 (100 µM) and SAICAR (4 µM), we demonstrated that PNS (10 µg/mL) inhibited STAT3 phosphorylation and Th17 cell differentiation by suppressing nuclear PKM2 accumulation. In CIA mice, PNS attenuated CIA symptoms, reduced the number of splenic Th17 cells and nuclear PKM2/STAT3 signaling. DISCUSSION AND CONCLUSIONS: PNS inhibited Th17 cell differentiation through the inhibition of nuclear PKM2-mediated STAT3 phosphorylation. PNS may be useful for treating RA.

Triptolide inhibits Th17 differentiation via controlling PKM2-mediated glycolysis in rheumatoid arthritis.

CONTEXT: Rheumatoid arthritis (RA) is an autoimmune disease with the aberrant differentiation of T helper 17 (Th17) cells. Pyruvate kinase M2 (PKM2), a key enzyme of glycolysis, was associated with Th17 cell differentiation. AIM: To investigate the potential therapeutic effects of triptolide (TP) in collagen-induced arthritis (CIA) and Th17 cell differentiation, and elucidated the underlying mechanisms. METHODS: PKM2 expression and IL-17A production in peripheral blood of RA patients were detected by RT-qPCR or ELISA. Flow cytometry and ELISA were employed to assess the effect of Th17 cell differentiation by TP. PKM2 expression and other glycolysis-related factors were detected using RT-qPCR and Western Blot. PKM2 specific inhibitor Compound 3 K was used to verify the mechanisms. Male DBA/1J mice were divided into control, model, and TP (60 µg/kg) groups to assess the anti-arthritis effect, Th17 cell differentiation and PKM2 expression. RESULTS: PKM2 expression positively correlated with IL-17A production in RA patients. PKM2 expression was increased upon Th17 cell differentiation. Down-regulating PKM2 expression could strongly reduce Th17 cell differentiation. Molecular docking analysis predicted that TP targeted PKM2. TP treatment significantly reduced Th17 cell differentiation, PKM2 expression, pyruvate, and lactate production. In addition, compared with down-regulating PKM2 alone (Compound 3 K treatment), co-treatment with TP and Compound 3 K further significantly decreased PKM2-mediated glycolysis and Th17 cell differentiation. In CIA mice, TP repressed the PKM2-mediated glycolysis and attenuated joint inflammation. CONCLUSION: TP inhibited Th17 cell differentiation through the inhibition of PKM2-mediated glycolysis. We highlight a novel strategy for the use of TP in RA treatment.

MicroRNA-26b-5p alleviates murine collagen-induced arthritis by modulating Th17 cell plasticity.

Rheumatoid arthritis (RA) is a chronic autoimmune disease, and the abnormal differentiation of IL-17-producing T helper (Th17) cells is an important factor in the pathogenesis. Previous studies have shown that microRNAs (miRNAs, miR) act as key regulators of Th17 cells. However, the effects of miRNAs on Th17 cell differentiation and plasticity in RA are not clear. In this study, not only low miR-26b-5p expression and high IL-17A level were observed in the peripheral blood of RA patients, but also the negative correlation between miR-26b-5p and IL-17A was explored. The changes in collagen-induced arthritis (CIA) mice were consistent with those in RA patients. The results of in vitro experiments showed that miR-26b-5p mainly inhibited the initial differentiation of Th17 cells but did not impact the differentiation of induced-Treg into Th17-like cells. Meanwhile, miR-26b-5p mimics treatment alleviated inflammatory responses and reduced Th17 proportion in CIA mice. These results indicated that miR-26b-5p could alleviate the development of mice CIA by inhibiting the excessive Th17 cells, and that miR-26b-5p could modulate the plasticity of Th17 cell differentiation in RA, mainly block the initial differentiation. This may provide a novel strategy for the clinical treatment of RA.

A modified Wald test for reference scaled assessment of analytical equivalence.

For the reference scaled equivalence hypothesis to reduce the deficiency of the current practice in analytical equivalence assessment, the Wald test with Constrained Maximum Likelihood Estimate (CMLE) of the standard error was proposed to improve the efficiency when the sample sizes of test and reference product lots are small, and variances are unequal. However, by using the Wald test with CMLE standard error, simulations show that the type I error rate is below the nominal significance level. We proposed the Modified Wald test with CMLE standard error by replacing the maximum likelihood estimate of reference standard deviation with the sample estimate (MWCMLE), resulting in further improvement of type I error rate and power over the Wald test with CMLE standard error. In this paper, we further compare the proposed MWCMLE method to the Exact-test-Based (EB) method and the Generalized Pivotal Quantity (GPQ) method with equal or unequal variances, or equal or unequal sample sizes of both product lots. The simulations show that the proposed MWCMLE method outperforms the other two methods in type I error rate control and power improvement.

Non-inferiority tests for binary endpoints with variable margins.

Non-inferiority comparison between binary response rates of test and reference treatments is often performed in clinical studies. The most common approach to assess non-inferiority is to compare the difference between the estimated response rates with some margin. Previous methods use a variety of margins, including fixed margin, step-wise constant margin, and piece-wise smooth margin, where the latter two are functions of the reference response rate. The fixed margin approach assumes that the margin can be determined from historical trials with the consistent difference between the reference treatment and placebo, which may not be available. The step-wise constant margin approach suffers discontinuity in the power function which can cause trouble in sample size determination. Furthermore, many methods ignore the variability in margins dependent on the estimated reference response rate, leading to poor type I error control and power function approximation. In this study, we propose a variable margin approach to overcome the difficulties in fixed and step-wise constant margin approaches. We discuss several test statistics and evaluate their performance through simulation studies.

Statistical considerations regarding correlated lots in analytical biosimilar equivalence test.

In the evaluation of the analytical similarity data, an equivalence testing approach for most critical and quantitative quality attributes, which are assigned to Tier 1 in their proposed three-tier approach, was proposed. The Food and Drug Administration (FDA) has recommended the proposed equivalence testing approach to sponsors through meeting comments for Pre-Investigational New Drug Applications (PINDs) and Investigational New Drug Applications (INDs) since 2014. The FDA has received some feedback on the statistical issues of potentially correlated reference lot values subjected to equivalence testing since independent and identical observations (lot values) from the proposed biosimilar product and the reference product are assumed. In this article, we describe one method for correcting the estimation bias of the reference variability so as to increase the equivalence margin and its modified versions for increasing the equivalence margin and correcting the standard errors in the confidence intervals, assuming that the lot values are correlated under a few known correlation matrices. Our comparisons between these correcting methods and no correction for bias in the reference variability under several assumed correlation structures indicate that all correcting methods would increase the type I error rate dramatically but only improve the power slightly for most of the simulated scenarios. For some particular simulated cases, the type I error rate can be extremely large (e.g., 59%) if the guessed correlation is larger than the assumed correlation. Since the source of a reference drug product lot is unknown in nature, correlation between lots is a design issue. Hence, to obtain independent reference lot values by purchasing the reference lots at a wide time window often is a design remedy for correlated reference lot values.

Bioequivalence evaluation of sparse sampling pharmacokinetics data using bootstrap resampling method.

Bioequivalence studies are an essential part of the evaluation of generic drugs. The most common in vivo bioequivalence study design is the two-period two-treatment crossover design. The observed drug concentration-time profile for each subject from each treatment under each sequence can be obtained. AUC (the area under the concentration-time curve) and Cmax (the maximum concentration) are obtained from the observed drug concentration-time profiles for each subject from each treatment under each sequence. However, such a drug concentration-time profile for each subject from each treatment under each sequence cannot possibly be available during the development of generic ophthalmic products since there is only one-time point measured drug concentration of aqueous humor for each eye. Instead, many subjects will be assigned to each of several prespecified sampling times. Then, the mean concentration at each sampling time can be obtained by the simple average of these subjects' observed concentration. One profile of the mean concentration vs. time can be obtained for one product (either the test or the reference product). One AUC value for one product can be calculated from the mean concentration-time profile using trapezoidal rules. This article develops a novel nonparametric method for obtaining the 90% confidence interval for the ratio of AUCT and AUCR (or CT,max/CR,max) in crossover studies by bootstrapping subjects at each time point with replacement or bootstrapping subjects at all sampling time points with replacement. Here T represents the test product, and R represents the reference product. It also develops a novel nonparametric method for estimating the standard errors (SEs) of AUCh and Ch,max in parallel studies by bootstrapping subjects treated by the hth product at each time point with replacement or bootstrapping subjects treated by the hth product at all sampling time points with replacement, h = T, R. Then, 90% confidence intervals for AUCT/AUCR and CT,max/CR,max are obtained from the nonparametric bootstrap resampling samples and are used for the evaluation of bioequivalence study for one-time sparse sampling data.

Development of statistical methods for analytical similarity assessment.

To evaluate the analytical similarity between the proposed biosimilar product and the US-licensed reference product, a working group at Food and Drug Administration (FDA) developed a tiered approach. This proposed tiered approach starts with a criticality determination of quality attributes (QAs) based on risk ranking of their potential impact on product quality and the clinical outcomes. Those QAs characterize biological products in terms of structural, physicochemical, and functional properties. Correspondingly, we propose three tiers of statistical approaches based on the levels of stringency in requirements. The three tiers of statistical approaches will be applied to QAs based on the criticality ranking and other factors. In this article, we discuss the statistical methods applicable to the three tiers of QA. We further provide more details for the proposed equivalence test as the Tier 1 approach. We also provide some discussion on the statistical challenges of the proposed equivalence test in the context of analytical similarity assessment.

Checking distributional assumptions for pharmacokinetic summary statistics based on simulations with compartmental models.

Bioequivalence (BE) studies are an essential part of the evaluation of generic drugs. The most common in vivo BE study design is the two-period two-treatment crossover design. AUC (area under the concentration-time curve) and Cmax (maximum concentration) are obtained from the observed concentration-time profiles for each subject from each treatment under each sequence. In the BE evaluation of pharmacokinetic crossover studies, the normality of the univariate response variable, e.g. log(AUC)1 or log(Cmax), is often assumed in the literature without much evidence. Therefore, we investigate the distributional assumption of the normality of response variables, log(AUC) and log(Cmax), by simulating concentration-time profiles from two-stage pharmacokinetic models (commonly used in pharmacokinetic research) for a wide range of pharmacokinetic parameters and measurement error structures. Our simulations show that, under reasonable distributional assumptions on the pharmacokinetic parameters, log(AUC) has heavy tails and log(Cmax) is skewed. Sensitivity analyses are conducted to investigate how the distribution of the standardized log(AUC) (or the standardized log(Cmax)) for a large number of simulated subjects deviates from normality if distributions of errors in the pharmacokinetic model for plasma concentrations deviate from normality and if the plasma concentration can be described by different compartmental models.

Statistical evaluation of several methods for cut-point determination of immunogenicity screening assay.

The cut point of the immunogenicity screening assay is the level of response of the immunogenicity screening assay at or above which a sample is defined to be positive and below which it is defined to be negative. The Food and Drug Administration Guidance for Industry on Assay Development for Immunogenicity Testing of Therapeutic recommends the cut point to be an upper 95 percentile of the negative control patients. In this article, we assume that the assay data are a random sample from a normal distribution. The sample normal percentile is a point estimate with a variability that decreases with the increase of sample size. Therefore, the sample percentile does not assure at least 5% false-positive rate (FPR) with a high confidence level (e.g., 90%) when the sample size is not sufficiently enough. With this concern, we propose to use a lower confidence limit for a percentile as the cut point instead. We have conducted an extensive literature review on the estimation of the statistical cut point and compare several selected methods for the immunogenicity screening assay cut-point determination in terms of bias, the coverage probability, and FPR. The selected methods evaluated for the immunogenicity screening assay cut-point determination are sample normal percentile, the exact lower confidence limit of a normal percentile (Chakraborti and Li, 2007) and the approximate lower confidence limit of a normal percentile. It is shown that the actual coverage probability for the lower confidence limit of a normal percentile using approximate normal method is much larger than the required confidence level with a small number of assays conducted in practice. We recommend using the exact lower confidence limit of a normal percentile for cut-point determination.

Statistical considerations in setting product specifications.

According to ICH Q6A (1999), a specification is defined as a list of tests, references to analytical procedures, and appropriate acceptance criteria, which are numerical limits, ranges, or other criteria for the tests described. For drug products, specifications usually consist of test methods and acceptance criteria for assay, impurities, pH, dissolution, moisture, and microbial limits, depending on the dosage forms. They are usually proposed by the manufacturers and subject to the regulatory approval for use. When the acceptance criteria in product specifications cannot be pre-defined based on prior knowledge, the conventional approach is to use data from a limited number of clinical batches during the clinical development phases. Often in time, such acceptance criterion is set as an interval bounded by the sample mean plus and minus two to four standard deviations. This interval may be revised with the accumulated data collected from released batches after drug approval. In this article, we describe and discuss the statistical issues of commonly used approaches in setting or revising specifications (usually tighten the limits), including reference interval, (Min, Max) method, tolerance interval, and confidence limit of percentiles. We also compare their performance in terms of the interval width and the intended coverage. Based on our study results and review experiences, we make some recommendations on how to select the appropriate statistical methods in setting product specifications to better ensure the product quality.

Using tolerance intervals for assessment of pharmaceutical quality.

In quality control of drug products, tolerance intervals are commonly used methods to assure a certain proportion of the products covered within a pre-specified acceptance interval. Depending on the nature of the quality attributes, the corresponding acceptance interval could be one-sided or two-sided. Thus, the tolerance intervals can also be one-sided or two-sided. To better utilize tolerance intervals for quality assurance, we reviewed the computation method and studied their statistical properties in terms of batch acceptance probability in this article. We also illustrate the application of one-sided and two-sided tolerance, as well as two one-sided tests through the examples of dose content uniformity test, delivered dose uniformity test, and dissolution test.

Quality assurance test of delivered dose uniformity of multiple-dose inhaler and dry powder inhaler drug products.

The delivered dose uniformity is one of the most critical requirements for dry powder inhaler (DPI) and metered dose inhaler products. In 1999, the Food and Drug Administration (FDA) issued a Draft Guidance entitled Nasal Spray and Inhalation Solution, Suspension, and Spray Drug Products-Chemistry, Manufacturing and Controls Documentation and recommended a two-tier acceptance sampling plan that is a modification of the United States Pharmacopeia (USP) sampling plan of dose content uniformity (USP34<601>). This sampling acceptance plan is also applied to metered dose inhaler (MDI) and DPI drug products in general. The FDA Draft Guidance method is shown to have a near-zero probability of acceptance at the second tier. In 2000, under the request of The International Pharmaceutical Aerosol Consortium, the FDA developed a two-tier sampling acceptance plan based on two one-sided tolerance intervals (TOSTIs) for a small sample. The procedure was presented in the 2005 Advisory Committee Meeting of Pharmaceutical Science and later published in the Journal of Biopharmaceutical Statistics (Tsong et al., 2008). This proposed procedure controls the probability of the product delivering below a pre-specified effective dose and the probability of the product delivering over a pre-specified safety dose. In this article, we further propose an extension of the TOSTI procedure to single-tier procedure with any number of canisters.

Letter to the editor by the authors of Exact Calculation of Power and Sample Size in Bioequivalence Studies Using Two One-sided Tests, Pharmaceutical Statistics, DOI: 10.1002/pst.1666.

This article reflects the views of the authors and should not be construed to be those of the US Food and Drug Administration.

Exact calculation of power and sample size in bioequivalence studies using two one-sided tests.

The number of subjects in a pharmacokinetic two-period two-treatment crossover bioequivalence study is typically small, most often less than 60. The most common approach to testing for bioequivalence is the two one-sided tests procedure. No explicit mathematical formula for the power function in the context of the two one-sided tests procedure exists in the statistical literature, although the exact power based on Owen's special case of bivariate noncentral t-distribution has been tabulated and graphed. Several approximations have previously been published for the probability of rejection in the two one-sided tests procedure for crossover bioequivalence studies. These approximations and associated sample size formulas are reviewed in this article and compared for various parameter combinations with exact power formulas derived here, which are computed analytically as univariate integrals and which have been validated by Monte Carlo simulations. The exact formulas for power and sample size are shown to improve markedly in realistic parameter settings over the previous approximations.

Equivalence tests for interchangeability based on two one-sided probabilities.

A test treatment is considered to be interchangeable with its reference treatment if they are equivalent and expected to produce the same clinical result in any given patient. To assess interchangeability, FDA Draft Guidance (1999) and Guidance for Industry (2001, 2003) recommend using individual bioequivalence (IBE) and population bioequivalence (PBE) procedures. Chow (1999) and Chow and Liu (1999) gave a discussion on the limitation of the aggregate criteria of the IBE and PBE proposed therein. They mentioned that it is not clear whether IBE or PBE can imply average bioequivalence. Alternative approaches have been proposed to address the weakness of IBE and PBE. Dong et al. (2014) discuss the tolerance interval method and an approximate test for interchangeability defined by a two-sided probability. These tests may not be able to test for the two one-sided tests (TOST) with asymmetric margins around the true mean difference. In addition, the tests of two-sided probability provide no direction when failing the equivalence in interchangeability. Thus, we reexamine the statistical properties of the two one-sided tolerance interval approaches proposed by Tsong and Shen (2007, 2008). In this project, we extend their approach for parallel arms trials and paired/crossover data without the assumption of equal sample sizes and variances. We also develop the exact power function and assess the type I error rate of our proposed approach. In addition, we study the sample size determination based on the interchangeability testing utilizing the tolerance interval method.

Current status and needs of in-service training for psychiatric nurses in 24 provinces of China: a cross-sectional survey.

Aim: To investigate the current situation and need for post-competence training for psychiatric nurses in China and provide a reference for the development of training programs for psychiatric nurses. Design: A cross-sectional design. Methods: A cross-sectional study was conducted from August to October 2023 with 435 psychiatric nurses from 34 hospitals in 24 provinces of mainland China. A self-administered questionnaire was used for data collection. Descriptive statistics, non-parametric tests, and chi-square tests were used for data analysis. Results: The training content for psychiatric nurses is extensive, and the training load is large. Psychiatric nurses have high training demands for first aid knowledge, emergency handling ability, and anti-riot skills. Nurses with different years of experience have different training needs. The training needs of psychiatric nurses in specialized and general hospitals also different. Conclusion: The training status of psychiatric nurses is not consistent with the demand. Managers should combine this with psychiatric nurses' own work needs to develop practical and effective training programs.