Jogarao Gobburu

Challenges in developing drugs for pediatric CNS disorders: A focus on psychopharmacology.

ABSTRACT Many psychiatric and behavioral disorders manifest in childhood (attention deficit hyperactivity disorder, obsessive compulsive disorder, anxiety, depression, schizophrenia, autism spectrum disorder, etc.) and the opportunity for intervening early may attenuate full development of the disorder and lessen long term disability. Yet, pediatric drug approvals for CNS indications are limited, and pediatric testing generally occurs only after establishing adult efficacy, more as an afterthought rather than with the initial goal of developing the medication for a pediatric CNS indication. With pharmaceutical companies decreasing funding of their neuroscience research divisions overall, the prospects for moving promising investigational drugs forward into pediatrics will only decline. The goal of this review is to highlight important challenges around pediatric drug development for psychiatric disorders, specifically during clinical development, and to present opportunities for filling these gaps, using new strategies for de‐risking investigational drugs in new clinical trial designs/models. We will first present the current trends in pediatric drug efficacy testing in academic research and in industry trials, we will then discuss the regulatory landscape of pediatric drug testing, including policies intended to support and encourage more testing. Obstacles that remain will then be presented, followed by new designs, funding opportunities and considerations for testing investigational drugs safely.

Allometry Is a Reasonable Choice in Pediatric Drug Development

Pharmacokinetics (PK) plays a key role in bridging drug efficacy and safety from adults to pediatric patients. The principal purpose of projecting dosing in pediatrics is to guide trial design, not to waive the study per se. This research was designed to evaluate whether the allometric scaling (AS) approach is a satisfactory method to design PK studies in pediatric patients aged 2 years and older. We systematically evaluated drugs that had pediatric label information updated from 1998 to 2015. Only intravenous (IV) or oral administration drugs with available PK information in both children and adults from FDA‐approved labels were included. The allometric scaling approach was used to extrapolate adult clearance to pediatric clearance. The relative difference between the observed and the allometric scaling approach–predicted clearance was summarized and used to evaluate the predictive power of the allometric scaling approach. A total of 36 drugs eliminated by a metabolic pathway and 10 drugs by the renal pathway after intravenous (IV) or oral administration were included. Regardless of the administration route, elimination pathway, and age group, the allometric scaling approach can predict clearance in pediatric patients within a 2‐fold difference; 18 of the included drugs were predicted within a 25% difference, and 31 drugs within a 50% difference. The allometric scaling approach can adequately design PK studies in pediatric subjects 2 years and older.

Mechanistic Population Pharmacokinetics of Morphine in Neonates With Abstinence Syndrome After Oral Administration of Diluted Tincture of Opium.

Conducting and analyzing clinical trials in vulnerable neonates are extremely challenging. The aim of this analysis is to develop a morphine population pharmacokinetics (PK) model using data collected during a randomized control trial in neonates with abstinence syndrome (NAS). A 3‐compartment morphine structural PK model after intravenous (IV) administration from previously published work was utilized as prior, whereas an allometric scaling method with physiological consideration was used to extrapolate a PK profile from adults to pediatrics. The absorption rate constant and bioavailability were estimated in NAS after oral administration of diluted tincture of opium (DTO). Goodness‐of‐fit plots along with normalized prediction distribution error and bootstrap method were performed for model evaluation. We successfully extrapolated the PK profile from adults to pediatrics after IV administration. The estimated first‐order absorption rate constant and bioavailability were 0.751 hour−1 and 48.5%, respectively. Model evaluations showed that the model can accurately and precisely describe the observed data. The population pharmacokinetic model we derived for morphine after oral administration of DTO is reasonable and acceptable; therefore, it can be used to describe the PK and guide future studies. The integration of the previous population PK knowledge as prior information successfully overcomes the logistic and practical issue in vulnerable neonate population.

Characterizing Class-Specific Exposure-Viral Load Suppression Response of HIV Antiretrovirals Using A Model-Based Meta-Analysis

We applied model‐based meta‐analysis of viral suppression as a function of drug exposure and in vitro potency for short‐term monotherapy in human immunodeficiency virus type 1 (HIV‐1)‐infected treatment‐naïve patients to set pharmacokinetic targets for development of nonnucleoside reverse transcriptase inhibitors (NNRTIs) and integrase strand transfer inhibitors (InSTIs). We developed class‐specific models relating viral load kinetics from monotherapy studies to potency normalized steady‐state trough plasma concentrations. These models were integrated with a literature assessment of doses which demonstrated to have long‐term efficacy in combination therapy, in order to set steady‐state trough concentration targets of 6.17‐ and 2.15‐fold above potency for NNRTIs and InSTIs, respectively. Both the models developed and the pharmacokinetic targets derived can be used to guide compound selection during preclinical development and to predict the dose–response of new antiretrovirals to inform early clinical trial design.

Don't Get Boxed In: Commentary on the Visual Inspection Practices to Assess Exposure‐Response Relationships From Binary Clinical Variables

B plots are a quick and convenient way of displaying groups of data graphically. Typical box plots are highlighted by 5 key components: a boxed region consisting of the lower quartile, median, and upper quartile, and outer whiskers that, depending on methods used, may represent the lowest and highest observation or the lowest/highest observations within 1.5 of the interquartile (25th-75th) range. Because of the ease in constructing box plots and the convenience in representing several groups of data at once, box plots are commonly used in clinical pharmacology to assess the impact of extrinsic/ intrinsic factors (eg, sex, genetic polymorphisms, race, presence/absence of concomitant medications) on exposure (or, to a lesser extent, pharmacokinetic parameters). Another common use of box plots is to assess differences in effectiveness or adverse event occurrence relative to exposure. In these analyses, the original clinical measures are assigned as binary clinical outcomes (eg, yes or no, 1 or 0). Examples of binary clinical outcomes include occurrence of major bleeding episodes for anticoagulants, complete tumor response for oncology therapeutics, pain relief for analgesics, and reduction of pathogen levels below a prespecified limit for antivirals. In fact, continuous clinical variables are often converted to a binary response because of their ease of interpretation and utility as a clinical end point. The analysis of categorical data in this manner will often serve as a first step in deciding on the utility of additional analyses. For exposureresponse analysis (ER), box plots are commonly used to visually identify if different responses (eg, event vs nonevent) are associated with different exposures. Initial exposure–response evaluation for these binary variables typically includes the following 2 steps to determine the need for further quantitative analysis:

Exposure–Response Analysis of Alvocidib (Flavopiridol) Treatment by Bolus or Hybrid Administration in Newly Diagnosed or Relapsed/Refractory Acute Leukemia Patients

Purpose: To elucidate any differences in the exposure–response of alvocidib (flavopiridol) given by 1-hour bolus or a hybrid schedule (30-minute bolus followed by a 4-hour infusion) using a flavopiridol/cytosine arabinoside/mitoxantrone sequential protocol (FLAM) in patients with acute leukemia. The hybrid schedule was devised to be pharmacologically superior in chronic leukemia based on unbound exposure. Experimental Design: Data from 129 patients in three FLAM studies were used for pharmacokinetic/pharmacodynamic modeling. Newly diagnosed (62%) or relapsed/refractory (38%) patients were treated by bolus (43%) or hybrid schedule (57%). Total and unbound flavopiridol concentrations were fit using nonlinear mixed-effect population pharmacokinetic methodologies. Exposure–response relationships using unbound flavopiridol AUC were explored using recursive partitioning. Results: Flavopiridol pharmacokinetic parameters were estimated using a two-compartment model. No pharmacokinetic covariates were identified. Flavopiridol fraction unbound was 10.9% and not different between schedules. Partitioning found no association between dosing schedule and clinical response. Clinical response was associated with AUC ≥ 780 h*ng/mL for newly diagnosed patients and AUC ≥ 1,690 h*ng/mL for relapsed/refractory patients. Higher exposures were not associated with increases in severe adverse events (≥ grade 3). Conclusions: Pharmacokinetic modeling showed no difference in flavopiridol plasma protein binding for bolus versus hybrid dosing. Further trials in newly diagnosed patients with acute leukemia should utilize the bolus FLAM regimen at the MTD of 50 mg/m2/day. Trials in relapsed/refractory patients should use the hybrid dosing schedule at the MTD (30/60 mg/m2/day) to achieve the higher exposures required for maximal efficacy in this population. Clin Cancer Res; 23(14); 3592–600. ©2017 AACR.

Parent‐Metabolite Pharmacokinetic Modeling and Pharmacodynamics of Veliparib (ABT‐888), a PARP Inhibitor, in Patients With BRCA 1/2–Mutated Cancer or PARP‐Sensitive Tumor Types

Veliparib (ABT‐888) is a novel oral poly‐ADP‐ribose polymerase (PARP) inhibitor that is being developed for the treatment of hematologic malignancies and solid tumors. Although the pharmacokinetics of veliparib have been studied in combination with cytotoxic agents, limited information exists regarding the pharmacokinetics (PK) of chronically dosed single‐agent veliparib in patients with either BRCA 1/2–mutated cancer or PARP‐sensitive tumors. The objectives of the current analysis were to characterize the population pharmacokinetics of veliparib and its primary, active metabolite, M8, and to evaluate the relationship between veliparib and M8 concentrations and poly‐ADP‐ribose (PAR) level observed in peripheral blood mononuclear cells (PBMCs). Seventy‐one subjects contributed with veliparib plasma concentrations, M8 plasma concentrations, and PAR levels in PBMCs. Veliparib and M8 concentrations were modeled simultaneously using a population PK approach. A 2‐compartment model with delayed first‐order absorption and the elimination parameterized as renal (CLR/F) and nonrenal clearance (CLNR/F) adequately described veliparib pharmacokinetics. The pharmacokinetics of the M8 metabolite was described with a 2‐compartment model. Creatinine clearance(CLCR) and lean body mass (LBM) were identified as significant predictors of veliparib CLR/F and central volume of distribution, respectively. For a typical subject (LBM, 48 kg; CLCR, 95 mL/min), total clearance (CLR/F + CLNR/F), and central and peripheral volume of distribution for veliparib were estimated as 17.3 L/h, 98.7 L, and 48.3 L, respectively. At least 50% inhibition of PAR levels in PBMCs was observed at dose levels ranging from 50 to 500 mg.

Population Pharmacokinetics of an Intranasally Administered Combination of Oxymetazoline and Tetracaine in Healthy Volunteers

The primary objective of the current investigation was to establish the pharmacokinetic characteristics of oxymetazoline and tetracaines primary metabolite, para‐butylaminobenzoic acid (PBBA), after the intranasal administration of oxymetazoline/tetracaine. Thirty‐six subjects contributing a total of 1791 plasma concentration results from 2 open‐label trials were utilized. Model development was achieved using data from the second trial (N = 24) in which 0.3 mg oxymetazoline/18 mg tetracaine was administered. External model validation utilized data from the first trial (N = 12), which included doses of 0.3 mg oxymetazoline/18 mg tetracaine and 0.6 mg oxymetazoline/36 mg tetracaine. Oxymetazoline and PBBA dispositions were described by a 2‐compartment model with first‐order absorption. An allometric model for body weight was included on volumes and clearances to describe unexplained between‐subject variability. The final oxymetazoline parameter estimates were ka 4.41 h−1; peripheral volume 418 L; clearance 66.4 L/h; central volume 6.97 L; and intercompartmental clearance 419 L/h for a 70‐kg subject. The final PBBA parameter estimates were ka 8.51 h−1; peripheral volume 32.0 L; clearance 16.7 L/h; central volume 29.8 L; and intercompartmental clearance 2.43 L/h for a 70‐kg subject. Between‐subject variability ranged from 14% to 39% for oxymetazoline and from 10% to 94% for PBBA.

Drugs Against Rare Diseases: Are The Regulatory Standards Higher?

The US Food and Drug Administration (FDA) recently issued a draft Guidance for Industry for Rare Diseases: Common Issues in Drug Development (referred to as “Rare Diseases Guidance”). In our opinion, the FDA should consider: (a) explicitly acknowledging the standards are higher for rare diseases for the reasons presented in this article; and (b) illustrating innovative development pathways that may be acceptable for rare diseases, including case studies.

Should Pharmacometrics Be Training the Next R&D President?

We continue to train pharmacometric scientists primarily in methodology. The lack of training in business and drug development concepts, however, is preventing pharmacometricians from becoming high‐level decision makers. The more recent growth of opportunities in pharmacometrics is propelled by applications within both companies and regulatory agencies. However, these applications themselves may not lead to sustained growth of opportunities. How can we prepare pharmacometricians to fundamentally re‐engineer the drug development paradigm?