Scientific considerations for global drug development.

Requiring regional or in-country confirmatory clinical trials before approval of drugs already approved elsewhere delays access to medicines in low- and middle-income countries and raises drug costs. Here, we discuss the scientific and technological advances that may reduce the need for in-country or in-region clinical trials for drugs approved in other countries and limitations of these advances that could necessitate in-region clinical studies.

Report on the current status of the use of real-world data (RWD) and real-world evidence (RWE) in drug development and regulation.

Radically expanding use of real-world data (RWD) and real-world evidence (RWE) holds the potential to substantially impact drug development, pharmaceutical regulation, and payment within health care systems. Central to this is the reconfiguration of data gathering and transformation of data to information, which can be used as evidence for decision making. We discuss applications of this paradigm in the light of recent developments in both the United States and Europe on RWD and RWE.

Individualized treatment strategies for hyperuricemia informed by a semi-mechanistic exposure-response model of uric acid dynamics.

To provide insight into pharmacological treatment of hyperuricemia we developed a semi-mechanistic, dynamical model of uric acid (UA) disposition in human. Our model represents the hyperuricemic state in terms of production of UA (rate, PUA), its renal filtration (glomerular filtration rate, GFR) and proximal tubular reabsorption (fractional excretion coefficient, FE). Model parameters were estimated using data from 9 Phase I studies of xanthine oxidase inhibitors (XOI) allopurinol and febuxostat and a novel uricosuric, the selective UA reabsorption inhibitor lesinurad, approved for use in combination with a XOI. The model was qualified for prediction of the effect of patients' GFR and FE on concentration of UA in serum (sUA) and UA excretion in urine and their response to drug treatment, using data from 2 Phase I and 4 Phase III studies of lesinurad. Percent reduction in sUA from baseline by a XOI is predicted to be independent of GFR, FE or PUA. Uricosurics are more effective in underexcreters of UA or patients with normal GFR. Co-administration of a XOI and an uricosuric agent should be considered for patients with high sUA first in the treatment algorithm of gout before uptitration of XOI. The XOI dose in combination with a uricosuric can be reduced compared to XOI alone for the same target sUA to the degree dependent on patient's GFR and FE. This exposure-response model of UA can be used to rationally select the best drug treatment option to lower elevated sUA in gout patients under differing pathophysiological situations.

Poor medication adherence in clinical trials: consequences and solutions.

Poor adherence to medicines in clinical trials can undermine the value of the trials; for example, by compromising estimates of the benefits and risks of a medicine. In this article, we highlight such consequences and also discuss approaches to tackle this problem.

Determination of the starting dose in the first-in-human clinical trials with monoclonal antibodies: a systematic review of papers published between 1990 and 2013.

A systematic review was performed to evaluate how the maximum recommended starting dose (MRSD) was determined in first-in-human (FIH) studies with monoclonal antibodies (mAbs). Factors associated with the choice of each MRSD determination method were also identified. PubMed was searched for FIH studies with mAbs published in English between January 1, 1990 and December 31, 2013, and the following information was extracted: MRSD determination method, publication year, therapeutic area, antibody type, safety factor, safety assessment results after the first dose, and number of dose escalation steps. Seventy-nine FIH studies with mAbs were identified, 49 of which clearly reported the MRSD determination method. The no observed adverse effects level (NOAEL)-based approach was the most frequently used method, whereas the model-based approach was the least commonly used method (34.7% vs 16.3%). The minimal anticipated biological effect level (MABEL)- or minimum effective dose (MED)-based approach was used more frequently in 2011-2013 than in 1990-2007 (31.6% vs 6.3%, P=0.036), reflecting a slow, but steady acceptance of the European Medicines Agency's guidance on mitigating risks for FIH clinical trials (2007). The median safety factor was much lower for the MABEL- or MED-based approach than for the other MRSD determination methods (10 vs 32.2-53). The number of dose escalation steps was not significantly different among the different MRSD determination methods. The MABEL-based approach appears to be safer and as efficient as the other MRSD determination methods for achieving the objectives of FIH studies with mAbs faster.

Impact of the pharmaceutical sciences on health care: a reflection over the past 50 years.

During the last century, particularly the latter half, spectacular progress has been made in improving the health and longevity of people. The reasons are many, but the development of medicines has played a critical role. This report documents and reflects on the impressive contribution that those working in the pharmaceutical sciences have made to healthcare over the past 50 years. It is divided into six sections (drug discovery; absorption, distribution, metabolism, and excretion; pharmacokinetics and pharmacodynamics; drug formulation; drug regulation; and drug utilization), each describing key contributions that have been made in the progression of medicines, from conception to use. A common thread throughout is the application of translational science to the improvement of drug discovery, development, and therapeutic application. Each section has been coordinated by a leading scientist who was asked, after consulting widely with many colleagues across the globe, to identify "The five most influential ideas/concepts/developments introduced by 'pharmaceutical scientists' (in their field) over the past 50 years?" Although one cannot predict where the important breakthroughs will come in the future to meet the unmet medical needs, the evidence presented in this report should leave no doubt that those engaged in the pharmaceutical sciences will continue to make their contributions heavily felt.

Quantitative clinical pharmacology is transforming drug regulation.

Prior to 1970s, development and regulation of new drugs was devoid of a fully quantitative, pathophysiological conceptual foundation. Malcolm Rowland pioneered, in collaboration with colleagues and friends, our modern understanding of drug clearance concepts, and equipped drug development and regulatory scientists with key investigative tools such as physiologically-based pharmacokinetic (PBPK) modeling, standardized approaches to characterizing drug metabolism, and microdosing. From the 1970s to the present, Malcolm Rowland has contributed to key advances in pharmacokinetics that have had transformational impacts on drug regulatory science. These advances include concepts that have led to the fundamental understanding that mechanistically derived, quantitative variations in drug concentrations, rather than assigned dosage alone, drive pharmacodynamic effects (PKPD)-including disease biomarkers and clinical outcomes. This body of knowledge has transformed drug development and regulatory science theory and practice from naïve empiricism to a mechanism/model-based, quantitative scientific discipline. As a result, it is now possible to incorporate pre-clinical in vitro data on drug physico-chemical properties, metabolizing enzymes, transporters and permeability properties into PBPK-based simulations of expected PK distributions and drug-drug interactions in human populations. The most comprehensive application of PK-PD is in the modeling and simulation of clinical trials in the context of model-based drug development and regulation, imbedded in the "learn-confirm paradigm". Regulatory agencies have embraced these advances and incorporated them into regulatory requirements, approval acceleration pathways and regulatory decisions. These developments are reviewed here, with emphasis on key contributions of Malcolm Rowland that facilitated this transformation.

Simulation of correlated continuous and categorical variables using a single multivariate distribution.

Clinical trial simulations make use of input/output models with covariate effects; the virtual patient population generated for the simulation should therefore display physiologically reasonable covariate distributions. Covariate distribution modeling is one method used to create sets of covariate values (vectors) that characterize individual virtual patients, which should be representative of real subjects participating in clinical trials. Covariates can be continuous (e.g., body weight, age) or categorical (e.g., sex, race). A modeling method commonly used for incorporating both continuous and categorical covariates, the Discrete method, requires the patient population to be divided into subgroups for each unique combination of categorical covariates, with separate multivariate functions for the continuous covariates in each subset. However, when there are multiple categorical covariates this approach can result in subgroups with very few representative patients, and thus, insufficient data to build a model that characterizes these patient groups. To resolve this limitation, an application of a statistical methodology (Continuous method) was conceived to enable sampling of complete covariate vectors, including both continuous and categorical covariates, from a single multivariate function. The Discrete and Continuous methods were compared using both simulated and real data with respect to their ability to generate virtual patient distributions that match a target population. The simulated data sets consisted of one categorical and two correlated continuous covariates. The proportion of patients in each subgroup, correlation between the continuous covariates, and ratio of the means of the continuous covariates in the subgroups were varied. During evaluation, both methods accurately generated the summary statistics and proper proportions of the target population. In general, the Continuous method performed as well as the Discrete method, except when the subgroups, defined by categorical value, had markedly different continuous covariate means, for which, in the authors' experience, there are few clinically relevant examples. The Continuous method allows analysis of the full population instead of multiple subgroups, reducing the number of analyses that must be performed, and thereby increasing efficiency. More importantly, analyzing a larger pool of data increases the precision of the covariance estimates of the covariates, thus improving the accuracy of the description of the covariate distribution in the simulated population.

Evidence of effectiveness: how much can we extrapolate from existing studies?

Drug development can be a science of extrapolation if the use of a drug exposure-response relationship is embraced and implemented through mechanistically oriented pharmacokinetic (PK)-pharmacodynamic (PD) modeling analysis and clinical trial simulation. The traditional requirement of at least 2 adequate and well-controlled phase III studies by the US Food and Drug Administration for drug approval can be waived in certain situations, substantially reducing the resources and time. In this article, the authors introduce a real drug development case where the chance for this exemption was maximized by actively using PK-PD modeling followed by clinical trial simulation, resulting in faster and more economical introduction of a new dosage regimen to patients.

Population pharmacokinetic analysis and simulation of the time-concentration profile of etanercept in pediatric patients with juvenile rheumatoid arthritis.

This study was performed to estimate the population pharmacokinetic (PK) parameters of etanercept in pediatric juvenile rheumatoid arthritis (JRA) patients and to compare the steady-state time-concentration profiles between etanercept 0.8-mg/kg once-weekly and 0.4-mg/kg twice-weekly subcutaneous (SC) regimens by clinical trial simulation. To this end, mixed-effect analysis (NONMEM, Version 5.1) was performed using the etanercept PK database consisting of 69 JRA patients (4-17 years). Based on the population PK parameters obtained herein, a Monte Carlo clinical trial simulation experiment was conducted to compare the PK profiles in 200 virtual JRA patients who randomly received either etanercept 0.4 mg/kg SC twice weekly or 0.8 mg/kg once weekly for 12 weeks. The following population PK model could adequately describe etanercept PK profiles for twice-weekly SC dosing of 0.4 mg/kg: CL/F (L/h)=0.0576 (female) or 0.0772 (male) x (body surface area in m2/1.071)1.41, V/F(L)=7.88 x (body weight in kg/30.8). The means +/- standard deviations of simulated trough concentrations for 0.8-mg/kg once-weekly and 0.4-mg/kg twice-weekly dosing regimens were 1.58 +/- 1.07 mg/L and 1.92 +/- 1.09 mg/L, respectively. Peaks during 0.8-mg/kg once-weekly dosing (2.92 +/- 1.41 mg/L) were only 11% higher than during 0.4 mg/kg twice-weekly dosing (2.62 +/- 1.23 mg/L). In conclusion, the clinical trial simulation confirmed that 0.8-mg/kg once-weekly and 0.4-mg/kg twice-weekly SC regimens of etanercept are expected to yield overlapping steady-state time-concentration profiles, leading to equivalent clinical outcomes. This has been the basis of the recent Food and Drug Administration approval of the 0.8-mg/kg once-weekly regimen in pediatric patients with JRA.