In oncology trials, patients are withdrawn from study at the time when progressive disease (PD) is diagnosed, which is defined as 20% increase of tumor size from the minimum. Such informative censoring can lead to biased parameter estimates when nonlinear mixed effects models are fitted using NONMEM. In this work, we investigated how empirical Bayes estimates (EBE) could be exploited to impute missing tumor size observations and partially correct biases in the parameter estimates. 50 simulated datasets, each consisting of 100 patients, were generated based on the published model. From the simulated dataset, censoring due to PD diagnosis has been implemented. Using the post-hoc EBEs acquired from fitting the censored datasets using NONMEM, imputed values were generated from the tumor size model. Model fitting was carried out using censored and imputed datasets. Parameter estimates using both datasets were compared with true values. Tumor growth rate and cell kill rate were approximately 28% and 16% underestimated when fitted using the censored dataset, respectively. With the imputed datasets, relative biases of tumor growth rate and cell kill rate decreased to about 6% and 0%, respectively. Our work demonstrates that using EBEs acquired from fitting the model to the censored dataset and imputing the unknown tumor size observations with individual predictions beyond the PD time point is a viable option to solve the bias associated with structural parameter estimates. This approach, however, would not be helpful in getting better estimates of variance parameters.
Bays ; Bias (Epidemiology)* ; Dataset ; Diagnosis ; Humans ; Methods*

Model-based approaches have emerged as important tools for quantitatively understanding temporal relationships between drug dose, concentration, and effect over the course of treatment, and have now become central to optimal drug development and tailored drug treatment. In oncology, the therapeutic index of a chemotherapeutic drug is typically narrow and a full dose–response relationship is not available, often because of treatment failure. Noting the benefits of model-based approaches and the low therapeutic index of oncology drugs, in recent years, modeling approaches have been increasingly used to streamline oncologic drug development through early identification and quantification of dose–response relationships. With this background, this report reviews publications that used model-based approaches to evaluate drug treatment outcome variables in oncology therapeutics, ranging from tumor size dynamics to tumor/biomarker time courses and survival response.
Antineoplastic Agents/*administration & dosage/pharmacology ; Biomarkers, Tumor ; *Dose-Response Relationship, Drug ; Humans ; Medical Oncology ; *Models, Biological ; Neoplasms/*drug therapy/pathology

Drug repurposing, or repositioning, is to identify new uses for existing drugs. Significantly reducing the costs and time-to-market of a medication, drug repurposing has been an alternative tool to accelerate drug development process. On the other hand, ‘real world data (RWD)’ has been also increasingly used to support drug development process owing to its better representing actual pattern of drug treatment and outcome in real world. In the healthcare domain, RWD refers to data collected from sources other than traditional clinical trials; for example, in electronic health records or claims and billing data. With the enactment of the 21st Century Cures Act, which encourages the use of RWD in drug development and repurposing as well, such increasing trend in RWD use will be expedited. In this context, this review provides an overview of recent progresses in the area of drug repurposing where RWD was used, by firstly introducing the increasing trend and regulatory change in the use of RWD in drug development, secondly reviewing published works using RWD in drug repurposing, classifying them in the repurposing strategy, and lastly addressing limitations and advantages of RWDs.

Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, ‘drug repurposing’ (or ‘drug repositioning’) has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.
Data Mining ; Drug Repositioning ; Machine Learning

Hyperlipidemia and hypertension are among the major risk factors for cardiovascular disease (CVD) and they often co-exist within a single patient. Recently, many studies published results regarding the potential role of statins in decreasing blood pressure (BP) however there is still a controversy over the efficacy of statin therapy on BP. This study aimed to investigate the potential role of rosuvastatin in BP lowering properties in Korean population. Data were taken from three randomized, multiple-dose cross over studies for rosuvastatin, angiotensin II receptor blocker (ARB) and metformin monotherapies and the combined therapy of rosuvastatin and ARB, in total of 91 healthy male normotensive subjects. Measurements of systolic blood pressure (SBP), diastolic blood pressure (DBP) at the baseline before treatment begins and for 24 hours after the last dose were used in the analysis. The analysis variables used were (i) the mean change in steady-state BP from the baseline, symbolized as ΔBP, and (ii) the difference in ΔBP between the ARB monotherapy and the combined therapy, symbolized as ΔBP,d. The ΔBP and ΔBP,d for SBP from each study varied in -0.1 ~ -1.3 mmHg and 1.2 ~ 1.6 mmHg, respectively, and were not significantly different from zero. The ΔBP and ΔBP,d for DBP from each study varied in -2.8 ~ -1.4 mmHg and -2.9 ~ -1.8, respectively, which were statistically significant for ΔBP (p < 0.05) but was not for ΔBP,d (p > 0.05). These results indicated that the rosuvastatin monotherapy may produce small blood pressure lowing effect in DBP.
Blood Pressure* ; Cardiovascular Diseases ; Cross-Over Studies ; Humans ; Hydroxymethylglutaryl-CoA Reductase Inhibitors* ; Hyperlipidemias ; Hypertension ; Male ; Metformin ; Receptors, Angiotensin ; Risk Factors ; Rosuvastatin Calcium*

Cilostazol is used for the treatment of intermittent claudication, ulceration and pain. This study was conducted to develop a population pharmacodynamic (PD) model for cilostazol's closure time (CT) prolongation effect in healthy Korean subjects based on a pharmacokinetic (PK) model previously developed. PD data were obtained from 29 healthy subjects who participated in a study conducted in 2009 at Severance Hospital. The PK model used was a two-compartment model with first order absorption. CT data were best described by a turnover model with a fractional turnover rate constant (K(out)) inhibited by drug effects (Eff), which were represented by a sigmoid E(max) model [Eff = E(max) · C(γ) / (EC₅₀(γ)+C(γ))] with E(max) being maximum drug effect, EC₅₀ drug plasma concentration at 50% of E(max), C drug plasma concentrations, and γ the Hill coefficient. For the selected PD model, parameter estimates were 0.613 hr⁻¹ for K(out), 0.192 for E(max), 730 ng/ml for EC₅₀ and 5.137 for γ. Sex and caffeine drinking status significantly influenced the baseline CT, which was 85.36 seconds in male non-caffeine drinkers and increased by 15.5% and 16.4% in females and caffeine drinkers, respectively. The model adequately described the time course of CT. This was the first population PD study for cilostazol's CT prolongation effect in a Korean population.
Absorption ; Caffeine ; Colon, Sigmoid ; Drinking ; Female ; Healthy Volunteers ; Humans ; Intermittent Claudication ; Male ; Plasma ; Ulcer

Characterizing the time course of baseline or pre-drug blood pressure is important in acquiring unbiased estimates of antihypertensive drug effect. In this study, we recruited 23 healthy male volunteers and measured systolic (SBP) and diastolic blood pressure (DBP) over 24 hours on an hourly basis. Using a non-linear mixed effects model, circadian rhythm observed in blood pressure measurements was described by incorporating two cosine functions with periods 24 and 12 hours. A mixture model was applied to identify subgroups exhibiting qualitatively different circadian rhythms. Our results suggested that 78% of the study population, defined as ‘dippers’, demonstrated a typical circadian profile with a morning rise and a nocturnal dip. The remaining 22% of the subjects defined as ‘non-dippers’, however, were not adequately described using the typical profile and demonstrated an elevation of blood pressure during night-time. Covariate search identified weight as being positively correlated with mesor of SBP. Visual predictive checks using 1,000 simulated datasets were performed for model validation. Observations were in agreement with predicted values in ‘dippers’, but deviated slightly in ‘non-dippers’. Our work is expected to serve as a useful reference in assessing systematic intra-day blood pressure fluctuations and antihypertensive effects as well as assessing drug safety of incrementally modified drugs.
Blood Pressure ; Circadian Rhythm ; Dataset ; Humans ; Male ; Volunteers

Weight is a covariate representative of body size and is known to influence drug disposition. Recently, with increased use of allometric scaling, this variable has become more significant in accounting for variability in pharmacokinetic parameters. In adults, weight can be considered as a time invariant covariate because physical development is complete. As a result, when weight is missing in data, the typical or median value (say, 70 kg) could be imputed. On the contrary, weight continuously changes with age in the pediatric population. In this case, it is more appropriate to consider different median weight for each age group. We constructed a prediction model for weight using postmenstrual age (PMA) with the data consisting of 83,014 Korean pediatric patients. Weight, PMA, and gender information were collected from electronic medical records. Sigmoid models multiplied by exponential or logistic function were tested for basic model structure. Covariate effects on model parameters were then investigated using selection criteria of p < 0.001. All analyses were performed using NONMEM 7.3.0 and R3.2.0. The sigmoid model multiplied by logistic function best described the data and there was a significant difference between boys and girls in model parameters. It is expected that the results obtained in this work can be used for imputation of missing weights in pediatrics when PMA is available. In addition, the developed model can be used for clinical studies in children under 12 years old whose weight change rapidly with age and for model building in dealing with time varying body weight as a covariate.
Adult ; Body Size ; Body Weight* ; Child ; Colon, Sigmoid ; Electronic Health Records ; Female ; Humans ; Patient Selection ; Pediatrics ; Weights and Measures

In generic drug development, comparative pharmacokinetic (PK) studies are conducted to assess equivalence in pharmacokinetics and safety profiles between test and reference formulations. However, there is no established quantitative approach available for safety assessment. This study aimed to propose a method for drug safety evaluation in generic drug development, as assessed by drug influence on blood pressure and heart rate change. Data were taken from a randomized, open label, 2-way cross-over comparative PK study for megestrol conducted in 39 healthy male volunteers. Vital signs of systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) were measured at 0 (pre-dose), 4, 8, 12, 24, 48, 72, 96 and 120 hours after the dose. Safety parameters used in the analysis were area under vital sign change versus time curve to the last measured time (AUVlast) and maximum vital sign change (Vmax). Considering highly variable nature of vital signs, the scaled bioequivalence approach developed by US FDA was adopted as a decision rule for safety evaluation between formulations. With the FDA scaled approach, 90% confidence intervals of geometric mean ratio for DBP, 0.7969~1.0377 for Vmax and 0.7304~1.0660 for AUVlast, were both included in the equivalence ranges of 0.7694~1.2997 and 0.6815~1.4674, respectively, and similarly, those for HR were included in their respective scaled equivalence limits, while SBP satisfied the conventional equivalence criterion of 0.8-1.25. These results illustrate the feasibility of applying the suggested approach in cardiovascular safety evaluation in a generic drug.
Blood Pressure ; Heart Rate ; Humans ; Male ; Megestrol ; Pharmacokinetics ; Therapeutic Equivalency ; Vital Signs ; Volunteers

One of the important purposes in population pharmacokinetic studies is to investigate the relationships between parameters and covariates to describe parameter variability. The purpose of this study is to evaluate the model's ability to correctly detect the parameter-covariate relationship that can be observed in phase I clinical trials. Data were simulated from a two-compartment model with zero-order absorption and first-order elimination, which was built from valsartan's concentration data collected from a previously conducted study. With creatinine clearance (CLCR) being used as a covariate to be tested, 3 different significance levels of 0.001 Absorption ; Clinical Trials, Phase I as Topic* ; Creatinine ; Dataset ; Healthy Volunteers ; Hope