FDA Approval Summary: Fruquintinib for the Treatment of Refractory Metastatic Colorectal Cancer.

On November 8, 2023, the FDA approved fruquintinib, an inhibitor of vascular endothelial growth factor receptors (VEGFR)-1, -2, and -3, for the treatment of patients with metastatic colorectal cancer (mCRC) who have been previously treated with fluoropyrimidine­, oxaliplatin­, and irinotecan­based chemotherapy, an anti­VEGF therapy, and, if RAS wild­type and medically appropriate, an anti EGFR therapy. Approval was based on Study FRESCO-2, a globally-conducted, double-blind, placebo-controlled randomized trial. The primary endpoint was overall survival (OS). The key secondary endpoint was progression-free survival (PFS). A total of 691 patients were randomized (461 and 230 into the fruquintinib and placebo arms, respectively). Fruquintinib provided a statistically significant improvement in OS with a hazard ratio (HR) of 0.66 (95% CI: 0.55, 0.80; p<0.001). The median OS was 7.4 months (95% CI: 6.7, 8.2) in the fruquintinib arm and 4.8 months (95% CI: 4.0, 5.8) for the placebo arm. Adverse events observed were generally consistent with the known safety profile associated with inhibition of the VEGFR. The results of FRESCO-2 were supported by the FRESCO study, a double-blind, single country, placebo-controlled, randomized trial in patients with refractory mCRC who have been previously treated with fluoropyrimidine­, oxaliplatin­, and irinotecan­based chemotherapy. In FRESCO, the OS HR was 0.65 (95% CI: 0.51, 0.83; p<0.001). FDA concluded that the totality of the evidence from FRESCO-2 and FRESCO supported an indication for patients with mCRC with prior treatment with fluoropyrimidine, oxaliplatin-, and irinotecan-based chemotherapy, an anti-VEGF biological therapy, and if RAS wild­type and medically appropriate, an anti-EGFR therapy.

Pharmacokinetic Models for Inhaled Fluticasone Propionate and Salmeterol Xinafoate to Quantify Batch-to-Batch Variability.

Advair Diskus is an essential treatment for asthma and chronic obstructive pulmonary disease. It is a dry powder inhaler with a combination of fluticasone propionate (FP) and salmeterol xinafoate (SX). However, the pharmacokinetics (PK) batch-to-batch variability of the reference-listed drug (RLD) hindered its generic product development. This work developed the PK models for inhaled FP and SX that could represent potential batch variability. Two batches each of the reference and the test product (R1, R2, T1, T2) of Advair Diskus (100 µg FP/50 µg SX inhalation) were administered to 60 healthy subjects in a 4-period, 4-sequence crossover study. The failure of the bioequivalence (BE) between R1 and R2 confirmed the high between-batch variability of the RLD. Non-linear mixed effect modeling was used to estimate the population mean PK parameters for each batch. For FP, a 2-compartment model with a sequential dual zero-order absorption best described the PK profile. For SX, a 2-compartment model with a first-order absorption model best fit the data. Both models were able to capture the plasma concentration, the maximum concentration, and the total exposure (AUCinf) adequately for each batch, which could be used to simulate the BE study in the future. In vitro properties were also measured for each batch, and the batch with a higher fraction of the fine particle (diameter < 1 µm, < 2 µm) had a higher AUCinf. This positive correlation for both FP and SX could potentially assist the batch selection for the PK BE study.

Use of the Same Model or Modeling Strategy Across Multiple Submissions: Focus on Complex Drug Products.

Evidence shows that there is an increasing use of modeling and simulation to support product development and approval for complex generic drug products in the USA, which includes the use of mechanistic modeling and model-integrated evidence (MIE). The potential for model reuse was the subject of a workshop session summarized in this review, where the session included presentations and a panel discussion from members of the U.S. Food and Drug Administration (FDA), academia, and the generic drug product industry. Concepts such as platform performance assessment and MIE standardization were introduced to provide potential frameworks for model reuse related to mechanistic models and MIE, respectively. The capability of models to capture formulation and product differences was explored, and challenges with model validation were addressed for drug product classes including topical, orally inhaled, ophthalmic, and long-acting injectable drug products. An emphasis was placed on the need for communication between FDA and the generic drug industry to continue to foster maturation of modeling and simulation that may support complex generic drug product development and approval, via meetings and published guidance from FDA. The workshop session provided a snapshot of the current state of modeling and simulation for complex generic drug products and offered opportunities to explore the use of such models across multiple drug products.

A developmental cascade model of adolescent peer relationships, substance use, and psychopathological symptoms from child maltreatment.

INTRODUCTION: Although previous studies have demonstrated high intercorrelations among deviant peer affiliation, substance use, externalizing and internalizing symptoms in adolescence, these studies have been limited because they did not examine 1) the associations over time by assuming one particular sequence; and 2) child maltreatment effects. METHODS: This study included 617 adolescents (54.3 % girls, 55.6 % Black) at-risk of maltreatment living in the U.S and primarily low-income. Deviant peer affiliation was assessed at ages 12, 14, and 16 using 13 items from the modified version of the Youth Risk Behavior and Monitoring the Future Survey. Externalizing and internalizing symptoms were measured at ages 12, 14, and 16 using the Child Behavior Checklist. The number of substances used (ages 12, 14, 16, and 18) were created by summing the self-reported alcohol, tobacco, and marijuana use. Each type of maltreatment (birth to age 12) was assessed using the self-report. RESULTS: Autoregressive cross-lagged structural equation modeling explained the stability effects within each domain, as well as how different maltreatment types affect diverse developmental processes. Cross-lagged results showed the socialization effects of peers on substance use, whereas the peer selection effects on externalizing symptoms. Physical abuse was only associated with externalizing symptoms, while sexual abuse was associated with both externalizing and internalizing symptoms. Additionally, emotional abuse was associated with deviant peer affiliation and substance use. CONCLUSIONS: Identifying the underlying reciprocal processes offers a deeper understanding of peer relationships in the substance use and externalizing symptoms among at-risk of maltreatment sample.

Establishing the suitability of model-integrated evidence to demonstrate bioequivalence for long-acting injectable and implantable drug products: Summary of workshop.

On November 30, 2021, the US Food and Drug administration (FDA) and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop titled "Establishing the Suitability of Model-Integrated Evidence (MIE) to Demonstrate Bioequivalence for Long-Acting Injectable and Implantable (LAI) Drug Products." This workshop brought relevant parties from the industry, academia, and the FDA in the field of modeling and simulation to explore, identify, and recommend best practices on utilizing MIE for bioequivalence (BE) assessment of LAI products. This report summerized presentations and panel discussions for topics including challenges and opportunities in development and assessment of generic LAI products, current status of utilizing MIE, recent research progress of utilizing MIE in generic LAI products, alternative designs for BE studies of LAI products, and model validation/verification strategies associated with different types of MIE approaches.

Regulatory utility of mechanistic modeling to support alternative bioequivalence approaches: A workshop overview.

On September 30 and October 1, 2021, the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics cosponsored a live virtual workshop titled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches." The overall aims of the workshop included (i) engaging the generic drug industry and other involved stakeholders regarding how mechanistic modeling and simulation can support their product development and regulatory submissions; (ii) sharing the current state of mechanistic modeling for bioequivalence (BE) assessment through case studies; (iii) establishing a consensus on best practices for using mechanistic modeling approaches, such as physiologically based pharmacokinetic modeling and computational fluid dynamics modeling, for BE assessment; and (iv) introducing the concept of a Model Master File to improve model sharing between model developers, industry, and the FDA. More than 1500 people registered for the workshop. Based on a postworkshop survey, the majority of participants reported that their fundamental scientific understanding of mechanistic models was enhanced, there was greater consensus on model validation and verification, and regulatory expectations for mechanistic modeling submitted in abbreviated new drug applications were clarified by the workshop.

Complex, Co-occurring Needs Patterns and Evidence-Based Service Planning for Families Involved in Foster Care: A Map for Research and Practice.

This study described the complexity of service need co-occurrence among foster care-involved families and identified prevalent patterns of needs to inform future evidence-based service planning research. We utilized state administrative child maltreatment records, and restricted data to cases where the child entered foster care in 2019 and the caseworker indicated the presence of at least one need from the Family Assessment of Needs and Strengths (FANS; n = 1631). We extracted all unique combinations of needs (i.e., needs profiles), and we used association rule mining to identify patterns within these profiles. A total of 780 unique needs profiles emerged among the 1631 cases, which we condensed into 78 patterns. Although the variability and complexity of needs profiles makes evidence-based service planning difficult, the present analysis mapped prevalent needs patterns to guide future research intended to assist caseworkers in this task. Identification of maltreatment determinants among families involved in foster care, and future research into the needs within different needs patterns that might undermine treatment effectiveness, may result in a better balance between parsimonious service plans and a full consideration of co-occurring service needs.

Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods.

Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA's ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA's ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r 2 = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.

Racial differences in early adolescent substance use: Child abuse types and family/peer substance use as predictors.

This study examined the associations among child abuse types, family/peer substance use, and adolescent substance use, as well as testing whether these associations vary by race. The sample was derived from the Longitudinal Studies of Child Abuse and Neglect (N = 562). Child sexual abuse, family substance use, and peer substance use were associated with a higher likelihood of adolescent substance use. Sexual abuse was more strongly associated with substance use in Black youth than in White youth. Conversely, greater peer substance use had a stronger association with substance use in White youth than in Black youth.

Considerations for Improving Metabolism Predictions for In Vitro to In Vivo Extrapolation.

High-throughput (HT) in vitro to in vivo extrapolation (IVIVE) is an integral component in new approach method (NAM)-based risk assessment paradigms, for rapidly translating in vitro toxicity assay results into the context of in vivo exposure. When coupled with rapid exposure predictions, HT-IVIVE supports the use of HT in vitro assays for risk-based chemical prioritization. However, the reliability of prioritization based on HT bioactivity data and HT-IVIVE can be limited as the domain of applicability of current HT-IVIVE is generally restricted to intrinsic clearance measured primarily in pharmaceutical compounds. Further, current approaches only consider parent chemical toxicity. These limitations occur because current state-of-the-art HT prediction tools for clearance and metabolite kinetics do not provide reliable data to support HT-IVIVE. This paper discusses current challenges in implementation of IVIVE for prioritization and risk assessment and recommends a path forward for addressing the most pressing needs and expanding the utility of IVIVE.

Quantitative methods and modeling to assess COVID-19-interrupted in vivo pharmacokinetic bioequivalence studies with two reference batches.

The coronavirus disease 2019 (COVID-19) has presented unprecedented challenges to the generic drug development, including interruptions in bioequivalence (BE) studies. Per guidance published by the US Food and Drug Administration (FDA) during the COVID-19 public health emergency, any protocol changes or alternative statistical analysis plan for COVID-19-interrupted BE study should be accompanied with adequate justifications and not lead to biased equivalence determination. In this study, we used a modeling and simulation approach to assess the potential impact of study outcomes when two different batches of a Reference Standard (RS) were to be used in an in vivo pharmacokinetic BE study due to the RS expiration during the COVID-19 pandemic. Simulations were performed with hypothetical drugs under two scenarios: (1) uninterrupted study using a single batch of an RS, and (2) interrupted study using two batches of an RS. The acceptability of BE outcomes was evaluated by comparing the results obtained from interrupted studies with those from uninterrupted studies. The simulation results demonstrated that using a conventional statistical approach to evaluate BE for COVID-19-interrupted studies may be acceptable based on the pooled data from two batches. An alternative statistical method which includes a "batch" effect to the mixed effects model may be used when a significant "batch" effect was found in interrupted four-way crossover studies. However, such alternative method is not applicable for interrupted two-way crossover studies. Overall, the simulated scenarios are only for demonstration purpose, the acceptability of BE outcomes for the COVID19-interrupted studies could be case-specific.

A Bayesian population physiologically based pharmacokinetic absorption modeling approach to support generic drug development: application to bupropion hydrochloride oral dosage forms.

We propose a Bayesian population modeling and virtual bioequivalence assessment approach to establishing dissolution specifications for oral dosage forms. A generalizable semi-physiologically based pharmacokinetic absorption model with six gut segments and liver, connected to a two-compartment model of systemic disposition for bupropion hydrochloride oral dosage forms was developed. Prior information on model parameters for gut physiology, bupropion physicochemical properties, and drug product properties were obtained from the literature. The release of bupropion hydrochloride from immediate-, sustained- and extended-release oral dosage forms was described by a Weibull function. In vitro dissolution data were used to assign priors to the in vivo release properties of the three bupropion formulations. We applied global sensitivity analysis to identify the influential parameters for plasma bupropion concentrations and calibrated them. To quantify inter- and intra-individual variability, plasma concentration profiles in healthy volunteers that received the three dosage forms, each at two doses, were used. The calibrated model was in good agreement with both in vitro dissolution and in vivo exposure data. Markov Chain Monte Carlo samples from the joint posterior parameter distribution were used to simulate virtual crossover clinical trials for each formulation with distinct drug dissolution profiles. For each trial, an allowable range of dissolution parameters ("safe space") in which bioequivalence can be anticipated was established. These findings can be used to assure consistent product performance throughout the drug product life-cycle and to support manufacturing changes. Our framework provides a comprehensive approach to support decision-making in drug product development.

Effect of Oral Ranitidine on Urinary Excretion of N-Nitrosodimethylamine (NDMA): A Randomized Clinical Trial.

Importance: In 2019, the US Food and Drug Administration (FDA) received a citizen petition indicating that ranitidine contained the probable human carcinogen N-nitrosodimethylamine (NDMA). In addition, the petitioner proposed that ranitidine could convert to NDMA in humans; however, this was primarily based on a small clinical study that detected an increase in urinary excretion of NDMA after oral ranitidine consumption. Objective: To evaluate the 24-hour urinary excretion of NDMA after oral administration of ranitidine compared with placebo. Design, Setting, and Participants: Randomized, double-blind, placebo-controlled, crossover clinical trial at a clinical pharmacology unit (West Bend, Wisconsin) conducted in 18 healthy participants. The study began in June 2020, and the end of participant follow-up was July 1, 2020. Interventions: Participants were randomized to 1 of 4 treatment sequences and over 4 periods received ranitidine (300 mg) and placebo (randomized order) with a noncured-meats diet and then a cured-meats diet. The cured-meats diet was designed to have higher nitrites, nitrates (nitrate-reducing bacteria can convert nitrates to nitrites), and NDMA. Main Outcome and Measure: Twenty-four-hour urinary excretion of NDMA. Results: Among 18 randomized participants (median age, 33.0 [interquartile range {IQR}, 28.3 to 42.8] years; 9 women [50%]; 7 White [39%], 11 African American [61%]; and 3 Hispanic or Latino ethnicity [17%]), 17 (94%) completed the trial. The median 24-hour NDMA urinary excretion values for ranitidine and placebo were 0.6 ng (IQR, 0 to 29.7) and 10.5 ng (IQR, 0 to 17.8), respectively, with a noncured-meats diet and 11.9 ng (IQR, 5.6 to 48.6) and 23.4 ng (IQR, 8.6 to 36.7), respectively, with a cured-meats diet. There was no statistically significant difference between ranitidine and placebo in 24-hour urinary excretion of NDMA with a noncured-meats diet (median of the paired differences, 0 [IQR, -6.9 to 0] ng; P = .54) or a cured-meats diet (median of the paired differences, -1.1 [IQR, -9.1 to 11.5] ng; P = .71). No drug-related serious adverse events were reported. Conclusions and Relevance: In this trial that included 18 healthy participants, oral ranitidine (300 mg), compared with placebo, did not significantly increase 24-hour urinary excretion of NDMA when participants consumed noncured-meats or cured-meats diets. The findings do not support that ranitidine is converted to NDMA in a general, healthy population. Trial Registration: ClinicalTrials.gov Identifier: NCT04397445.

Neighborhood structural characteristics, perceived neighborhood environment, and problem behaviors among at-risk adolescents.

Neighborhood environment has been linked to behavioral outcomes in adolescence. The current study examined two potential mediators (i.e., perceived social capital, perceived neighborhood disorder) in the association between neighborhood structural characteristics (i.e., neighborhood disorganization) and problem behaviors (i.e., externalizing behavior, substance use) among at-risk adolescents with prenatal substance exposure. The study sample included 350 15-year-old adolescents recruited at birth. Adolescents' addresses were linked to census tract data. Neighborhood structural characteristics were not directly associated with adolescent problem behaviors in the presence of perceived social capital and neighborhood disorder. Greater neighborhood disorganization was associated with lower levels of perceived social capital, which was related to greater perceived neighborhood disorder, and then problem behaviors. The findings suggest that community practice needs to focus on subjective perceptions of neighborhoods when developing intervention programs on problem behaviors among at-risk adolescents.

Using quantitative modeling tools to assess pharmacokinetic bias in epidemiological studies showing associations between biomarkers and health outcomes at low exposures.

Biomarkers of exposure can be measured at lower and lower levels due to advances in analytical chemistry. Using these sensitive methods, some epidemiology studies report associations between biomarkers and health outcomes at biomarker levels much below those associated with effects in animal studies. While some of these low exposure associations may arise from increased sensitivity of humans compared with animals or from species-specific responses, toxicology studies with drugs, commodity chemicals and consumer products have not generally indicated significantly greater sensitivity of humans compared with test animals for most health outcomes. In some cases, these associations may be indicative of pharmacokinetic (PK) bias, i.e., a situation where a confounding factor or the health outcome itself alters pharmacokinetic processes affecting biomarker levels. Quantitative assessment of PK bias combines PK modeling and statistical methods describing outcomes across large numbers of individuals in simulated populations. Here, we first provide background on the types of PK models that can be used for assessing biomarker levels in human population and then outline a process for considering PK bias in studies intended to assess associations between biomarkers and health outcomes at low levels of exposure. After providing this background, we work through published examples where these PK methods have been applied with several chemicals/chemical classes - polychlorinated biphenyls (PCBs), perfluoroalkyl substances (PFAS), polybrominated biphenyl ethers (PBDE) and phthalates - to assess the possibility of PK bias. Studies of the health effects of low levels of exposure will be improved by developing some confidence that PK bias did not play significant roles in the observed associations.

Bullying patterns and their associations with child maltreatment and adolescent psychosocial problems.

Introduction: This study aimed 1) to identify underlying heterogeneous patterns of bully-victim; 2) to examine whether the different types of child maltreatment predict the patterns of bully-victim; and 3) to investigate the association between patterns of bully-victim and adolescent psychosocial problems (depression, trouble at school, and substance use). Methods: This study included a sample of 1139 (48.7% girls, 53.4% Black) drawn from the Fragile Families and Child Wellbeing Study. Children's self-reported bullying victimization at age 9 was used using the Panel Study of Income Dynamics Child Development Supplement III. Teacher's reported bullying perpetration at age 9 was used using Social Skills Rating System. Child maltreatment types were assessed at age 5 using the Parent-Child Conflict Tactics Scale Coding. At age 15, adolescent depression was measured using modified Center for Epidemiologic Studies Depression Scale; trouble at school was measured using modified Add Health In-School Questionnaire; and self-reported substance use was used. Results: Latent class analysis produced four classes: bully-victim (19.8%), victim (16.3%), no bully-victim (38.9%), and bully (24.9%). Individuals who have been neglected are more likely to be in the victim class compared to all other classes. Physical abuse to be at heightened risk of involvement in the bully-victim, compared to victim class. Additionally, individuals in the victim group are greater risk for depression, problems at school, and alcohol, as compared to those in the other classes. Conclusions: This study augments the knowledge base on bully/victim, child maltreatment, and behavioral health outcomes and elucidates several suggestions for research and policy.

Kinetics of metabolism of deltamethrin and cis- and trans-permethrin in vitro. Studies using rat and human liver microsomes, isolated rat hepatocytes and rat liver cytosol.

The kinetics of metabolism of deltamethrin (DLM) and cis- and trans-permethrin (CPM and TPM) was studied in male Sprague-Dawley rat and human liver microsomes. DLM metabolism kinetics was also studied in isolated rat hepatocytes, liver microsomes and cytosol. Apparent intrinsic clearance (CLint) values for the metabolism of DLM, CPM and TPM by cytochrome P450 (CYP) and carboxylesterase (CES) enzymes in rat and human liver microsomes decreased with increasing microsomal protein concentration. However, when apparent CLint values were corrected for nonspecific binding to allow calculation of unbound (i.e., corrected) CLint values, the unbound values did not vary greatly with microsomal protein concentration. Unbound CLint values for metabolism of 0.05-1 µM DLM in rat liver microsomes (CYP and CES enzymes) and cytosol (CES enzymes) were not significantly different from rates of DLM metabolism in isolated rat hepatocytes. This study demonstrates that the nonspecific binding of these highly lipophilic compounds needs to be taken into account in order to obtain accurate estimates of rates of in vitro metabolism of these pyrethroids. While DLM is rapidly metabolised in vitro, the hepatocyte membrane does not appear to represent a barrier to the absorption and hence subsequent hepatic metabolism of this pyrethroid.

Self-Efficacy and Diabetes Self-Management in Middle-Aged and Older Adults in the United States: A Systematic Review.

OBJECTIVE | This study aimed to systematically review the existing literature on the relationship between self-efficacy and diabetes self-management in middle-aged and older adults in the United States and to determine whether the relationship applies across race and ethnicity. METHODS | Study selection followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. Studies published between 1990 to 2018 that investigated self-efficacy and diabetes self-management in middle-aged and older adults were searched using eight search engines: PsycINFO, CINAHL, SocIndex, AgeLine, MedLine, Social Science Citation Index, Cochrane Library, and Academic Search Complete. Only quantitative studies were included. RESULTS | Eleven studies met the inclusion criteria. Ten studies found significant association between self-efficacy and at least one self-management behavior, which included exercise, healthy diet, adherence to medication, blood glucose testing, and foot care. Findings were mixed regarding the role of self-efficacy in exercise and medication adherence. Higher self-efficacy in Mexican Americans predicted better self-management behaviors, whereas no relationship between self-efficacy and diabetes self-management was found in a sample of Black and White participants. The methodological quality of the studies was assessed. In general, the included studies demonstrated moderate methodological quality. Their limitations included inconsistency in the self-efficacy measures, a lack of longitudinal studies, and confounding bias. CONCLUSION | Self-efficacy has significant effects on self-management in middle-aged and older adults, but the effects may differ by race. Efforts to improve self-efficacy and deliver culturally appropriate services could potentially promote self-management behaviors in middle-aged and older adults with diabetes.