Driving Efficiency: Leveraging Model-Informed Approaches in 505(b)(2) Regulatory Actions.

Introduced by the Hatch-Waxman Amendments of 1984, 505(b)(2) applications permit the US Food and Drug Administration to rely, for approval of a new drug application, on information from studies not conducted by or for the applicant and for which the applicant has not obtained a right of reference. This pathway is designed to circumvent the unnecessary duplication of studies already conducted on a previously approved drug. It can lead to a considerably more efficient and expedited route to approval compared to a traditional development path. Model-informed drug development refers to the utilization of a diverse array of quantitative models in drug development to streamline the decision-making process. In this approach, diverse quantitative models that integrate knowledge of physiology, disease processes, and drug pharmacology are employed to address drug development challenges and guide regulatory decisions. Integration of these model-informed approaches into 505(b)(2) regulatory submissions and decision-making can further expedite the approval of new drugs. This article discusses some applications of model-informed approaches that were used to support 505(b)(2) drug development and regulatory actions. Specifically, various quantitative models such as population pharmacokinetic and exposure-response models have been employed to provide evidence of effectiveness, guide dosing in subgroups such as subjects with hepatic or renal impairment, and inform policies. These case study examples collectively underscore the significance of model-informed approaches in drug development and regulatory decisions associated with 505(b)(2) submissions.

Replicate Testing of Clinical Endpoints Can Prevent No-Go Decisions for Beneficial Vaccines.

In vaccine efficacy trials, inaccurate counting of infection cases leads to systematic under-estimation-or "dilution"-of vaccine efficacy. In particular, if a sufficient fraction of observed cases are false positives, apparent efficacy will be greatly reduced, leading to unwarranted no-go decisions in vaccine development. Here, we propose a range of replicate testing strategies to address this problem, considering the additional challenge of uncertainty in both infection incidence and diagnostic assay specificity/sensitivity. A strategy that counts an infection case only if a majority of replicate assays return a positive result can substantially reduce efficacy dilution for assays with non-systematic (i.e., "random") errors. We also find that a cost-effective variant of this strategy, using confirmatory assays only if an initial assay is positive, yields a comparable benefit. In clinical trials, where frequent longitudinal samples are needed to detect short-lived infections, this "confirmatory majority rule" strategy can prevent the accumulation of false positives from magnifying efficacy dilution. When widespread public health screening is used for viruses, such as SARS-CoV-2, that have non-differentiating features or may be asymptomatic, these strategies can also serve to reduce unneeded isolations caused by false positives.

Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease.

Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies.

Neurodegenerative Diseases: The Value of Early Predictive End Points.

Use of early predictive biomarkers of neurodegenerative disease in phase I clinical trials may improve the translation of novel drug therapies from preclinical development through late-stage studies. This article provides a categorical summary of promising biomarker approaches or clinical end points in molecular, cellular, metabolic, electrophysiological, or clinical function that can be used to predict or quantify the progression of neurodegenerative disorders and guide program support.

Minimal brain PBPK model to support the preclinical and clinical development of antibody therapeutics for CNS diseases.

There are several antibody therapeutics in preclinical and clinical development, industry-wide, for the treatment of central nervous system (CNS) disorders. Due to the limited permeability of antibodies across brain barriers, the quantitative understanding of antibody exposure in the CNS is important for the design of antibody drug characteristics and determining appropriate dosing regimens. We have developed a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain for antibody therapeutics, which was reduced from an existing multi-species platform brain PBPK model. All non-brain compartments were combined into a single tissue compartment and cerebral spinal fluid (CSF) compartments were combined into a single CSF compartment. The mPBPK model contains 16 differential equations, compared to 100 in the original PBPK model, and improved simulation speed approximately 11-fold. Area under the curve ratios for minimal versus full PBPK models were close to 1 across species for both brain and plasma compartments, which indicates the reduced model simulations are similar to those of the original model. The minimal model retained detailed physiological processes of the brain while not significantly affecting model predictability, which supports the law of parsimony in the context of balancing model complexity with added predictive power. The minimal model has a variety of applications for supporting the preclinical development of antibody therapeutics and can be expanded to include target information for evaluating target engagement to inform clinical dose selection.

A Disease Progression Model to Quantify the Nonmotor Symptoms of Parkinson's Disease in Participants With Leucine-Rich Repeat Kinase 2 Mutation.

Leucine-rich repeat kinase 2 (LRRK2) inhibitors are currently in clinical development as interventions to slow progression of Parkinson's disease (PD). Understanding the rate of progression in PD as measured by both motor and nonmotor features is particularly important in assessing the potential therapeutic effect of LRRK2 inhibitors in clinical development. Using standardized data from the Critical Path for Parkinson's Unified Clinical Database, we quantified the rate of progression of the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I (nonmotor aspects of experiences of daily living) in 158 participants with PD who were carriers and 598 participants with PD who were noncarriers of at least one of three different LRRK2 gene mutations (G2019S, R1441C/G, or R1628P). Age and disease duration were found to predict baseline disease severity, while presence of at least one of these three LRRK2 mutations was a predictor of the rate of MDS-UPDRS Part I progression. The estimated progression rate in MDS-UPDRS Part I was 0.648 (95% confidence interval: 0.544, 0.739) points per year in noncarriers of a LRRK2 mutation and 0.259 (95% confidence interval: 0.217, 0.295) points per year in carriers of a LRRK2 mutation. This analysis demonstrates that the rate of progression based on MDS-UPDRS Part I is ~ 60% lower in carriers as compared with noncarriers of LRRK2 gene mutations.

Extrapolation of Adult Efficacy to Pediatric Patients With Chemotherapy-Induced Nausea and Vomiting.

Chemotherapy-induced nausea and vomiting (CINV) is a common treatment-related adverse event that negatively impacts the quality of life of cancer patients. During pediatric drug development, extrapolation of efficacy from adult to pediatric populations is a pathway that can minimize the exposure of children to unnecessary clinical trials, improve efficiency, and increase the likelihood of success in obtaining a pediatric indication. The acceptability of the use of extrapolation depends on a series of evidence-based assumptions regarding the similarity of disease, response to intervention, and exposure-response relationships between adult and pediatric patients. This study evaluated publicly available summaries of data submitted to the US Food and Drug Administration for drugs approved for CINV to assess the feasibility of extrapolation for future development programs. Extracted data included trial design, emetogenic potential of chemotherapy, primary end points, participant enrollment criteria, and antiemetic pharmacokinetics. Adult and pediatric clinical trial designs for assessment of efficacy and safety shared key design elements. Antiemetic drugs found to be efficacious in adults were also efficacious in pediatric patients. Systemic drug concentrations at approved doses were similar for ondansetron, granisetron, and aprepitant, but an exposure-response analysis of palonosetron in children suggested that higher palonosetron systemic exposure is necessary for the prevention of CINV in the pediatric population. For 5-hydroxytryptamine-3 and neurokinin-1 receptor antagonist antiemetic drugs, efficacy in adults predicts efficacy in children, supporting the extrapolation of effectiveness of an antiemetic product in children from adequate and well-controlled studies in adult patients with CINV.

Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science.

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson's Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

Use of a Systems Pharmacology Model Based Approach Toward Dose Optimization of Parathyroid Hormone Therapy in Hypoparathyroidism.

We present an application of a quantitative systems pharmacology (QSP) model to support a regulatory decision, specifically in assessing the adequacy of the proposed dosing regimen. On January 23, 2015, the US Food and Drug Administration (FDA) approved Natpara (human parathyroid hormone (PTH)) to control hypocalcemia in patients with hypoparathyroidism. Clinical trial results indicated that although once-daily PTH reduced calcium and vitamin D dose requirement while maintaining the normocalcemia, the regimen was not adequate to control hypercalciuria. We hypothesized that the lack of control on urinary calcium excretion was due to the short half-life of PTH. The QSP model-based simulations indicated that a more frequent dosing regimen may provide better control on hypercalciuria while maintaining normocalcemia. A postmarketing trial was recommended to assess pharmacokinetics (PKs) and pharmacodynamics (PDs) of PTH dose and dosing regimen. Although other modeling approaches may be feasible, in this specific case, QSP model-based simulations fulfilled the information gap to support recommendations of this postmarketing trial.

Quantitative Understanding of QTc Prolongation and Gender as Risk Factors for Torsade de Pointes.

Several risk factors for development of a potentially fatal ventricular arrhythmia, torsade de pointes, have been observed, including female gender. However, in most investigations, only few torsade events were included and/or rarely were postdose heart rate corrected QT (QTc) measurements included, as a surrogate of drug exposure. We developed a multivariate logistic regression model using data from 22,214 patients (33% women) with 84 torsade events (56% women) to evaluate the relationship between risk factors for torsade using data from four anti-arrhythmic drug development programs. Before model development, we evaluated different QT/QTc postdose metrics (average, maximum, etc.) to determine which QT metric should be included into the model. The developed multivariate model showed that, after accounting for known risk factors for torsade and postdose QTc, that female gender remained a significant risk factor for torsade.

Use of statistical and pharmacokinetic-pharmacodynamic modeling and simulation to improve decision-making: A section summary report of the trends and innovations in clinical trial statistics conference.

The application of modeling and simulation (M&S) methods to improve decision-making was discussed during the Trends & Innovations in Clinical Trial Statistics Conference held in Durham, North Carolina, USA on May 1-4, 2016. Uses of both pharmacometric and statistical M&S were presented during the conference, highlighting the diversity of the methods employed by pharmacometricians and statisticians to address a broad range of quantitative issues in drug development. Five presentations are summarized herein, which cover the development strategy of employing M&S to drive decision-making; European initiatives on best practice in M&S; case studies of pharmacokinetic/pharmacodynamics modeling in regulatory decisions; estimation of exposure-response relationships in the presence of confounding; and the utility of estimating the probability of a correct decision for dose selection when prior information is limited. While M&S has been widely used during the last few decades, it is expected to play an essential role as more quantitative assessments are employed in the decision-making process. By integrating M&S as a tool to compile the totality of evidence collected throughout the drug development program, more informed decisions will be made.

Esomeprazole FDA Approval in Children With GERD: Exposure-Matching and Exposure-Response.

OBJECTIVES: Food and Drug Administration approval of proton-pump inhibitors for infantile gastroesophageal reflux disease has been limited by intrapatient variability in the clinical assessment of gastroesophageal reflux disease. For children 1 to 17 years old, extrapolating efficacy from adults for IV esomeprazole was accepted. The oral formulation was previously approved in children. Exposure-response and exposure matching analyses were sought to identify approvable pediatric doses. METHODS: Intragastric pH biomarker comparisons between children and adults were conducted. Pediatric doses were selected to match exposures in adults and were based on population pharmacokinetic (PK) modeling and simulations with pediatric esomeprazole data. Observed IV or oral esomeprazole PK data were available from 50 and 117 children, between birth and 17 years, respectively, and from 65 adults, between 20 and 48 years. A population PK model developed using these data was used to simulate steady-state esomeprazole exposures for children at different doses to match the observed exposures in adults. RESULTS: Exposure-response relationships of intragastric pH measures were similar between children and adults. The PK simulations identified a dosing regimen for children that results in comparable steady-state area under the curve to that observed after 20 mg in adults. For IV esomeprazole, increasing the infusion duration to 10 to 30 minutes in children achieves matching Cmax values with adults. CONCLUSIONS: The exposure-matching analysis permitted approval of an esomeprazole regimen not studied directly in clinical trials. Exposure-response for intragastric pH-permitted approval for the treatment of gastroesophageal reflux disease in children in whom it was not possible to evaluate the adult primary endpoint, mucosal healing assessed by endoscopy.

Enrolling Adolescents in Disease/Target-Appropriate Adult Oncology Clinical Trials of Investigational Agents.

The enrollment of adolescents with cancer in clinical trials is much lower than that of younger pediatric patients. For adolescents with "adult-type" cancers, lack of access to relevant trials is cited as one of the reasons for this discrepancy. Adolescents are generally not eligible for enrollment in adult oncology trials, and initial pediatric trials for many drugs are conducted years later, often after the drug is approved. As a result, accrual of adolescents to these trials may be slow due to off-label use, prospectively collected safety and efficacy data are lacking at the time of initial approval, and, most importantly, these adolescents have delayed access to effective therapies. To facilitate earlier access to investigational and approved drugs for adolescent patients with cancer, and because drug exposure is most often similar in adolescents and adults, we recommend the inclusion of adolescents (ages 12-17) in disease- and target-appropriate adult oncology trials. This approach requires careful monitoring for any differential safety signals, appropriate pharmacokinetic evaluations, and ensuring that ethical requirements are met. Inclusion of adolescents in adult oncology trials will require the cooperation of investigators, cooperative groups, industry, institutional review boards, and regulatory agencies to overcome real and perceived barriers. Clin Cancer Res; 23(1); 9-12. ©2016 AACR.

Rendering the 3 + 3 Design to Rest: More Efficient Approaches to Oncology Dose-Finding Trials in the Era of Targeted Therapy.

Selection of the maximum tolerated dose (MTD) as the recommended dose for registration trials based on a dose-escalation trial using variations of an MTD/3 + 3 design often occurs in the development of oncology products. The MTD/3 + 3 approach is not optimal and may result in recommended doses that are unacceptably toxic for many patients and in dose reduction/interruptions that might have an impact on effectiveness. Instead of the MTD/3 + 3 approach, the authors recommend an integrated approach. In this approach, typically an adaptive/Bayesian model provides a general framework to incorporate and make decisions for dose escalation based on nonclinical data, such as animal efficacy and toxicity data; clinical data, including pharmacokinetics/pharmacodynamics data; and dose/exposure-response data for efficacy and safety. To improve dose-ranging trials, model-based estimation, rather than hypothesis testing, should be used to maximize and integrate the information gathered across trials and doses. This approach may improve identification of optimal recommended doses, which can then be confirmed in registration trials. Clin Cancer Res; 22(11); 2623-9. ©2016 AACR SEE ALL ARTICLES IN THIS CCR FOCUS SECTION, "NEW APPROACHES FOR OPTIMIZING DOSING OF ANTICANCER AGENTS".

Role of Quantitative Clinical Pharmacology in Pediatric Approval and Labeling.

Dose selection is one of the key decisions made during drug development in pediatrics. There are regulatory initiatives that promote the use of model-based drug development in pediatrics. Pharmacometrics or quantitative clinical pharmacology enables development of models that can describe factors affecting pharmacokinetics and/or pharmacodynamics in pediatric patients. This manuscript describes some examples in which pharmacometric analysis was used to support approval and labeling in pediatrics. In particular, the role of pharmacokinetic (PK) comparison of pediatric PK to adults and utilization of dose/exposure-response analysis for dose selection are highlighted. Dose selection for esomeprazole in pediatrics was based on PK matching to adults, whereas for adalimumab, exposure-response, PK, efficacy, and safety data together were useful to recommend doses for pediatric Crohn's disease. For vigabatrin, demonstration of similar dose-response between pediatrics and adults allowed for selection of a pediatric dose. Based on model-based pharmacokinetic simulations and safety data from darunavir pediatric clinical studies with a twice-daily regimen, different once-daily dosing regimens for treatment-naïve human immunodeficiency virus 1-infected pediatric subjects 3 to <12 years of age were evaluated. The role of physiologically based pharmacokinetic modeling (PBPK) in predicting pediatric PK is rapidly evolving. However, regulatory review experiences and an understanding of the state of science indicate that there is a lack of established predictive performance of PBPK in pediatric PK prediction. Moving forward, pharmacometrics will continue to play a key role in pediatric drug development contributing toward decisions pertaining to dose selection, trial designs, and assessing disease similarity to adults to support extrapolation of efficacy.

Exposure-Response of Palonosetron for Prevention of Chemotherapy-Induced Nausea and Vomiting in Pediatric Patients.

OBJECTIVES: Extrapolation of efficacy from adult populations to pediatrics may be appropriate if it is reasonable to assume that the 2 populations have similar disease progression and response to intervention. When full extrapolation of efficacy is deemed appropriate, the pediatric dose can be determined by "matching" exposure to a drug with that observed in adult patients. This approach has been used in certain therapeutic areas to alleviate the burden of pediatric clinical trials. We present here a case in which exposure matching is not appropriate. METHODS: Data analyses including pharmacokinetics and exposure-response were performed using data obtained from 2 pediatric chemotherapy-induced nausea and vomiting trials for intravenously administered palonosetron (Aloxi; a 5-HT3 receptor antagonist) injection and the results were compared with adult findings. RESULTS: At the approved doses for adults (0.25 mg) and pediatric patients (20 µg/kg), mean systemic exposure (area under the curve) of palonosetron in pediatric patients was approximately 3-fold higher than that in adults, whereas the response rate was similar between the 2 populations. Across pediatric patients, those younger than 6 years of age appeared to have a higher response than those ages 6 years or older, even though estimated systemic exposure was comparable between these age groups. CONCLUSIONS: Overall, these analyses provide an example in which pediatric and adult exposure data alone are insufficient to adequately identify effective pediatric doses and raise questions about the appropriateness of exposure matching for other drugs in the same therapeutic class. In such cases, pediatric dose-ranging and efficacy studies are needed.

FDA Approval: Blinatumomab.

On December 3, 2014, the FDA granted accelerated approval of blinatumomab (Blincyto; Amgen, Inc.) for treatment of Philadelphia chromosome-negative relapsed or refractory precursor B-cell acute lymphoblastic leukemia (R/R ALL). Blinatumomab is a recombinant murine protein that acts as a bispecific CD19-directed CD3 T-cell engager. The basis for the approval was a single-arm trial with 185 evaluable adults with R/R ALL. The complete remission (CR) rate was 32% [95% confidence interval (CI), 26%-40%], and the median duration of response was 6.7 months. A minimal residual disease response was achieved by 31% (95% CI, 25%-39%) of all patients. Cytokine release syndrome and neurologic events were serious toxicities that occurred. Other common (>20%) adverse reactions were pyrexia, headache, edema, febrile neutropenia, nausea, tremor, and rash. Neutropenia, thrombocytopenia, and elevated transaminases were the most common (>10%) laboratory abnormalities related to blinatumomab. A randomized trial is required in order to confirm clinical benefit.

On the formation of SFn (-) (n = 1-6) anions via a novel route and their properties.

RATIONALE: Sulfur hexafluoride (SF6 ) being a potential greenhouse gas, coupled with its numerous applications, makes the study of the formation and fragmentation of SF6 -based species important. The formation of SF6 -based anionic species has been studied using the gas feed-sputtering route and the mechanisms at play during the sputter-ejection of guest molecule-derived particles have also been probed. METHODS: Studies of the formation of SFn (-) (n = 1-6) anions were conducted from various surfaces (metal and compound) that were subjected to Cs(+) ion sputtering in the presence of SF6 gas employing the gas feed-cesium sputter technique. The anions generated were mass analyzed using a double-focusing magnetic sector mass spectrometer. Quantum mechanical computations were performed to study the ground state structure and stability of neutral and negatively charged SFn (n = 1-6) systems applying density functional theory (DFT) and ab initio methods (MP2 and CC). RESULTS: This technique readily generated (32) SFn (-) (n = 1-6) anions for all sizes of 'n' with practicable yields. Mass spectrometric measurements of the yield of sputter-ejected (32) SFn (-) (n = 1-6) anions reveal an oscillatory pattern as a function of 'n', with odd values of 'n' being relatively more abundant. The relative yield of (34) SFn (-) (n = 1-6) anions with respect to size was also measured albeit with low signal intensity. Also observed were F(-) , S(-) , F2 (-) , (33) SF5 (-) and (33) SF6 (-) anionic species. The relevant electron affinity and bond dissociation energy (BDE) values were also computed. CONCLUSIONS: Gas-phase SFn (-) (n = 1-6) anions can be effectively generated by using the gas spray-cesium sputter technique. Both experimental measurements and calculations indicate the existence of odd-even oscillations in the stability and electronic structure of the SFn (n = 1-6) systems. The highest yield recorded was for the sputter-ejected SF5 (-) species and this may be attributed to its 'superhalogen' anionic character coupled with the relatively favorable F(0) fragmentation pathway of sputtered SF6 (-) . A signature pertaining to intact SF6 (-) anion ejection is also observed.