Model-based Drug Development Research Articles

Применение данных реальной клинической практики с позиции клинической фармакологии при разработке лекарственных средств

The use of real-world data (RWD) and real-world evidence (RWE) is an international trend in the modern healthcare system. In a review article, the authors highlight the use of RWD/RWE for drug development in the following categories: drug-drug interactions; dose selection in special populations such as patients with elimination organ dysfunction; dosing optimization in children; the use of quantitative clinical pharmacology in model-based drug development; use of RWE as a parallel data source to complement a randomized controlled trial to extend indications; RWD/RWE to create an external control group for rare diseases.

Pharmacometrics: Application in Drug Development and Clinical Practice

In the last 4 years, pharmacometrics (PMX) has advanced to the point that it is now a crucial part of drug development. Drug delivery systems and molecules with more complex architecture are being developed as technology advances. Pharmacodynamic modelling is based on the quantitative integration of pharmacokinetics, pharmacological systems, and (patho-) physiological processes in order to comprehend the intensity and time course of drug effects on the body. As a result, the drug absorption and disposition processes after the administration of these drug delivery systems and engineered molecules become exceedingly complex. The research field of drug delivery focuses on the development of new techniques to manipulate drug absorption and disposition to achieve a desirable effect for the PMX model used. An opportunity to combine pharmacokinetic and pharmacodynamic model-based estimations with pharmacoeconomic models emerges given the unpredictability in the dose-concentration-effect relationship of medications. Model-based drug development (MBDD) has been found to address the underlying causes of medication failure, hence enhancing the productivity, effectiveness, and success of late-stage clinical research. The pharmacokinetic (PK) model principles in optimizing the drug dose to suit individual patient needs and achieving maximum therapeutic utility are called clinical pharmacology. Pharmacodynamics (PD) relates response to the concentration of drugs in the body. Disease progression model-based evaluation of disease progression is an important aspect of drug development and pharmacology. The future perspective of pharmacometrics in drug development and clinical practices is challenging.

Optimal dosing of gliclazide - a model-based approach.

Gliclazide was approved as a treatment for type 2 diabetes in an era before model-based drug development and consequently the recommended doses were not optimized with modern methods. To investigate various dosing regimens of gliclazide, we used publicly available data to characterise the dose-response relationship using pharmacometric models. A literature search identified 21 published gliclazide pharmacokinetic (PK) studies with full profiles. These were digitized and a PK model was developed for immediate- (IR) and modified-release (MR) formulations. Data from a gliclazide dose-ranging study of postprandial glucose were used to characterise the concentration-response relationship, using the integrated glucose-insulin model. Simulations from the full model showed that the maximum effect was 44% of the patients achieving HbA1c<7 % with 11% experiencing glucose<3 mmol/L and the most sensitive patients (i.e., 5% most extreme) experiencing 35 min of hypoglycaemia. Simulations revealed that the recommended IR dose (320 mg) was appropriate with no efficacy gain with increased dose. However, the recommended dose for the MR formulation may be increased to 270 mg, with more patients achieving HbA1c goals (i.e., HbA1c<7%) without a hypoglycaemic risk higher than the resulting risk from the recommended IR dose.

The potential and pitfalls of artificial intelligence in clinical pharmacology.

Artificial intelligence (AI) involves using data and algorithms to perform activities normally achieved through human intelligence. AI and its key component machine learning contextualize data and enhance decision making to transform how we operate, discover, and develop drugs. Transforming clinical pharmacology (CP) as AI-augmented CP (AI/CP) requires an ecosystem including digitized data collection, standardized processes, complementary technologies, and an ethical framework. This commentary aims to highlight the future perspectives of AI/CP in drug development.

Case Study on Certara’s Simcyp PBPK Simulator to Eliminate Lengthy Clinical Trails

Background/Purpose: Analysis and new interpretation of the existing information are equivalent to creating new knowledge. A case study is an example of exploratory research and allows researchers to analyze the available information using a systematic analysis framework. In this paper, a case study on a clinical research simulation software product called Simcyp is offered by a global company Certara to its clients in the pharmaceutical industry. Objective: To know the current status of model-based drug development simulation software, with special emphasis on Certara’s Simcyp software, its features, and variations, its client’s usage pattern to prepare new drugs, its usage in research and developmental contribution during the last 22 years, and analysis of Symcyp as a research division of Certara using SWOC framework and analysis of the Symcip as simulation software using ABCD analysis framework as stakeholder analysis. Design/Methodology/Approach: The case study uses an exploratory research approach where the information is collected from various sources including the company website, its competitor’s website, various magazine articles, and scholarly articles from Google Scholar. The collected information are analyzed systematically using company analysis frameworks, product analysis framework, SWOC analysis framework, and ABCD analysis framework. Findings/Result: Based on the analysis, it is found that Symcyp is a most admired simulation software in the pharmaceutical industry for model-based drug design and development for different varieties of diseases. The strategy of Simcyp division of Certara to satisfy, delight, and enlighten its clients is also discussed. Originality/Value: The case study is based on a systematic analysis of a software product of a company using company analysis frameworks, product analysis framework, SWOC analysis framework, and ABCD analysis framework thereby contributing to interpret the existing knowledge in a new way through new interpretation. Type of Paper: Academic research based case study.

Model-based approach to sampling optimization in studies of antibacterial drugs for infants and young children.

Clinical trials for pediatric indications and new pediatric drugs face challenges, including the limited blood volume due to the patients’ small bodies. In Japan, the Evaluation Committee on Unapproved or Off‐labeled Drugs with High Medical Needs has discussed the necessity of pediatric indications against the background of a lack of Japanese pediatric data. The limited treatment options regarding antibiotics for pediatric patients are associated with the emergence of antibiotic‐resistant bacteria. Regulatory guidelines promote the use of model‐based drug development to reduce practical and ethical constraints for pediatric patients. Sampling optimization is one of the key study designs for pediatric drug development. In this simulation study, we evaluated the precision of the empirical Bayes estimates of pharmacokinetic (PK) parameters based on the sampling times optimized by published pediatric population PK models. We selected three previous PK studies of cefepime and ciprofloxacin in infants and young children as paradigms. The number of sampling times was reduced from original full sampling times to two to four sampling times based on the Fisher information matrix. We observed that the precision of empirical Bayes estimates of the key PK parameters and the predicted efficacy based on the reduced sampling times were generally comparable to those based on the original full sampling times. The model‐based approach to sampling optimization provided a maximization of PK information with a minimum burden on infants and young children for the future development of pediatric drugs.

Koncept i upotreba populacionih farmakokinetičkih i farmakokinetičko/farmakodinamičkih modela u razvoju leka i kliničkoj praksi

Due to frequent clinical trial failures and consequently fewer new drug approvals, the need for improvement in drug development has, to a certain extent, been met using model-based drug development. Pharmacometrics is a part of pharmacology that quantifies drug behaviour, treatment response and disease progression based on different models (pharmacokinetic - PK, pharmacodynamic - PD, PK/PD models, etc.) and simulations. Regulatory bodies (European Medicines Agency, Food and Drug Administration) encourage the use of modelling and simulations to facilitate decision-making throughout all drug development phases. Moreover, the identification of factors that contribute to variability provides a basis for dose individualisation in routine clinical practice. This review summarises current knowledge regarding the application of pharmacometrics in drug development and clinical practice with emphasis on the population modelling approach.

Model Based Approach to Evaluate Isatuximab Monthly Dosing Regimen in Relapsed/Refractory Multiple Myeloma Patients

Background: Isatuximab (Isa) is a CD38 monoclonal antibody with multiple modes of action for killing tumor cells through direct tumor targeting and immune cell engagement (Moreno, Clin Cancer Res, 2019). The addition of Isa to pomalidomide (P) and dexamethasone (d) was associated with a significant and clinically meaningful benefit in progression-free survival (PFS) in heavily pre-treated patients with relapsed/refractory multiple myeloma (RRMM) (ICARIA-MM, NCT02514668) (Attal, Lancet, 2019). Isa, in combination with Pd, is approved in the United States, the European Union, Canada, Australia, Switzerland, and Japan for the treatment of adult patients with RRMM who have received at least two prior therapies including lenalidomide and a proteasome inhibitor. Aim: The objectives were to characterize the relationship between serum M-protein kinetics and PFS in RRMM using data from the Phase 3 ICARIA-MM study and to simulate expected longitudinal serum M-protein and PFS when switching to a hypothetical monthly dosing regimen after 6 months. Methods: A joint model of serum M-protein dynamics and PFS was developed using data from 256 evaluable ICARIA-MM patients. Patients received Isa intravenously at 10 mg/kg once weekly (QW) for 4 weeks, then every other week (Q2W) for 28-day cycles in combination with standard Pd (Isa-Pd) or Pd alone in the control arm. A tumor growth inhibition model was used to describe the serum M-protein kinetics under treatment effects of Isa-Pd or Pd alone, in which Isa exposure was predicted using individual PK parameters obtained from the population PK analysis (Fau, Population Approach Group in Europe, 2019) and Pd exposure was predicted from K-PD model using dosing history. Trial simulations were then performed using individual PK/PD parameters of ICARIA-MM patients to evaluate whether efficacy is maintained after switching to a monthly dosing regimen. Results: The joint model identified the instantaneous changes (slope) in serum M-protein as the best on-treatment predictor for PFS and also identified baseline patient characteristics impacting serum M-protein kinetics (serum albumin and serum β2 microglobulin on the baseline serum M-protein levels and the non-IgG type on the serum M-protein growth rate, the serum M-protein slope), and PFS (presence of plasmacytomas). Non-IgG MM patients have similar behavior on serum M-protein kinetics for the first 60 weeks even with higher exposure and similar progression free survival compared to IgG MM patients supporting the non-dose adjustment based on IgG status. Clinical trial simulation of the ICARIA-MM Isa-Pd regimen demonstrated that switching all patients on treatment at 6 months to a monthly Isa regimen would shorten the median time to progression (TTP) (i.e. increase in serum M-protein greater than 25% and an absolute increase greater than 5 g/L compared to nadir) by 4.1 weeks and would shorten median PFS by 2.3 weeks (from 14.03 to 13.45 months). Based on TTP criteria, patients with no risk of earlier progression (57.7%) due to 6 months switch tend to have lower baseline tumor burden (lower serum M-protein and lower percent of bone marrow plasma cell) and better prognostic factors (higher glomerular filtration rate, higher albumin, lower β2 microglobulin). At 6 months, 85% of these patients had predicted stable "at least" VGPR status. Conclusions: Trial simulations supported the choice of the approved isatuximab 10 mg/kg QW/Q2W regimen and showed that switching to a monthly Isa regimen after 6 months may reduce clinical benefit in overall population. However, a subpopulation of patients with good prognosis and obtaining stable at least VGPR status by 6 months may switch to a monthly regimen after 6 months without compromising disease progression risk; this hypothesis will be tested in a prospective clinical trial. Model-based drug development has been successfully applied to support treatment decisions in RRMM patients. Disclosures Thai: Sanofi: Current Employment. Gaudel-Dedieu:Sanofi: Current Employment. Cerou:Sanofi: Current Employment. Sebastien:Sanofi: Current Employment. van de Velde:Sanofi: Current Employment, Current equity holder in publicly-traded company. Semiond:Sanofi: Current Employment. Veyrat-Follet:Sanofi: Current Employment.

Latent process model of the 6-minute walk test in Duchenne muscular dystrophy : A Bayesian approach to quantifying rare disease progression.

Duchenne muscular dystrophy (DMD) is a rare X-linked genetic pediatric disease characterized by a lack of functional dystrophin production in the body, resulting in muscle deterioration. Lower body muscle weakness progresses to non-ambulation typically by early teenage years, followed by upper body muscle deterioration and ultimately death by the late twenties. The objective of this study was to enhance the quantitative understanding of DMD disease progression through nonlinear mixed effects modeling of the population mean and variability of the 6-min walk test (6MWT) clinical endpoint. An indirect response model with a latent process was fit to digitized literature data using full Bayesian estimation. The modeling data set consisted of 22 healthy controls and 218 DMD patients from one interventional and four observational trials. The model reasonably described the central tendency and population variability of the 6MWT in healthy subjects and DMD patients. An exploratory categorical covariate analysis indicated that there was no apparent effect of corticosteroid administration on DMD disease progression. The population predicted 6MWT began to rise at 1.32 years of age, plateauing at 654 meters (m) at 17.2 years of age for the healthy population. The DMD trajectory reached a maximum of 411 m at 8.90 years before declining and falling below 1 m at age 18.0. The model has potential to be used as a Bayesian estimation and posterior simulation tool to make informed model-based drug development decisions that incorporate prior knowledge with new data.

Exposure-Response Analyses and Disease Modeling for Selection and Confirmation of Optimal Dosing Regimen of Isatuximab in Combination Treatment in Patients with Multiple Myeloma

Background: Isatuximab (Isa) is an anti-CD38 monoclonal antibody with multiple modes of action for killing tumor cells through direct tumor targeting and immune cell engagement. Exposure-Response (E-R) analysis and disease modelling of tumor burden were performed to evaluate the relationship between Isa exposure and efficacy outcomes. This analysis also supports the Isatuximab dosing regimen selection/confirmation when administered in combination in relapsed/refractory multiple myeloma (RRMM) patients. Methods: To determine the optimal dose for the Phase 3 ICARIA-MM study, E-R analyses were conducted in 44 and 52 RRMM evaluable patients from two Phase 1 trials of Isa in combination with pomalidomide/dexamethasone (Pd) (NCT02283775) or with lenalidomide/dexamethasone (Rd) (NCT01749969), respectively. Isa was administered intravenously at doses from 3 to 20 mg/kg every 2 weeks or weekly for 4 weeks then every 2 weeks (QW/Q2W). In the E-R analyses, several Isa exposure parameters were tested to evaluate if they were predictors of response (partial response [PR] or better). Baseline covariates were also considered in the model to evaluate potential confounding effects. In addition, a second type of analysis was performed on 2 pooled datasets of 153 (single agent and combination with Pd) and 162 (single agent and combination with Rd) evaluable RRMM patients: disease response was also captured by modeling the dynamics of the serum M-protein using a joint model of tumor growth inhibition and drop-out. Trial simulations were then performed to evaluate different dosing regimens of interest. To confirm the dose, E-R analyses were conducted in 297 evaluable RRMM patients from the phase 3 trial (ICARIA-MM; NCT02990338) comparing Isa-Pd (N=148) and Pd (N=149). Isa was administered intravenously at 10 mg/kg QW/Q2W in the Isa-Pd arm. Cox and Weibull (Progression-Free Survival [PFS]) or logistic regression (overall response rate [ORR]) models were used. Results: In the two combination Phase 1 trials, Isa appeared to be well tolerated with no clear dose response relationship between 10 and 20 mg/kg. E-R analyses suggested that higher Isa exposure represented by log Ctrough at 4 weeks (CT4W) was associated with increased ORR. Both models predicted an increased ORR with decreased beta-2 microglobulin and lower number of prior lines of therapy (Rd only), together with higher log CT4W. Clinical trial simulations showed that the probability of success to reach the targeted ORR was high with 10 mg/kg QW/Q2W for both Pd (83% for ORR ≥60%) and Rd (96% for ORR ≥50%). Serum M-protein reduction was greater at doses ≥10 mg/kg compared to doses &lt;10 mg/kg, with minimal differences between 10 and 20 mg/kg QW/Q2W. These analyses supported the rationale for 10 mg/kg QW/Q2W selection for the Phase 3 combination study. In the Phase 3 ICARIA-MM study, the E-R analysis showed that higher CT4W was associated with higher ORR (predicted ORR below and above the median CT4W being 52% and 72%, respectively). The final model also included time since diagnosis and Revised International Staging System (R-ISS) stage at baseline and an interaction between time since diagnosis and R-ISS; ORR increased as time since diagnosis increased and decreased with higher R-ISS stage. For PFS, in addition to CT4W, the model included plasmacytomas, serum albumin level and R-ISS at baseline. The predicted distribution of PFS indicated that patients with greater Isa CT4W had longer PFS, patients with R-ISS Stage III and plasmacytomas had shorter PFS. Matched analyses to Pd indicated that even patients with concentrations ≤median CT4W benefited from Isa treatment (PFS hazard ratio: 0.773). Conclusion: Model-based drug development was successfully applied to support Phase 3 Isa dosing regimen selection for use in combination with Pd in RRMM patients. E-R and clinical data from the Phase 3 ICARIA-MM study confirmed the efficacy and safety of 10 mg/kg QW/Q2W dose/regimen selected. Disclosures Rachedi: Sanofi: Employment. Koiwai:Sanofi: Employment. Gaudel-Dedieu:Sanofi: Employment. Sebastien:Sanofi: Employment. Thai:Sanofi: Employment. El-Cheikh:Sanofi: Employment. Brillac:Sanofi: Employment. Fau:Sanofi: Employment. Nguyen:Sanofi: Employment. Liu:Sanofi: Employment. Campana:Sanofi: Employment. van de Velde:Sanofi: Employment. Semiond:Sanofi: Employment.

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology.

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

Quantification of video-oculography as a biomarker for remyelination – An example from fampridine in internuclear ophthalmoplegia

A recent study showed that video-oculography was sensitive to the effect of fampridine on ocular movement speed and amplitude in internuclear ophthalmoplegia (INO), resulting from improved nerve transduction (Kanhai et al. 2019, doi: https://doi.org/10.1111/cns.13096). This can be used as a biomarker of remyelination for the investigation of new therapeutic compounds in multiple sclerosis (MS). We aimed to quantify the acute effect of fampridine on peak velocity versional dysconjugacy index (PV-VDI) and first peak amplitude (FPA-VDI) of eye movements. Population modelling was used to quantify FPA-VDI and PV-VDI for each eye in 23 patients with INO (10 unilateral, 13 bilateral), aged 31–72 yo. Correlations between eyes were taken into account, and fampridine potency and maximum efficacy were estimated according to a sigmoidal relationship. Fampridine effect on PV-VDI and FPA-VDI in each eye was accurately described and predicted (Fig. 1). Fampridine could maximally improve PV-VDI and FPA-VDI by 32.8% and 30.7% respectively, with 8.43 mg/L the concentration at which 50% of efficacy was reached. Repetitive testing worsened scores by around 0.8% for each additional test. Strong correlation was found for baseline PV-VDI and FPA-VDI between each other and between occasions. All parameters were estimated with good precision (RSE <30%). Baseline characteristics and fampridine effect were quantified for the new remyelination biomarker of video-oculography. Model-based drug development allows more efficient clinical trials using optimal design and clinical trial simulation exercises (Milligan et al. 2013, doi:https://doi.org/10.1038/clpt.2013.54), our model enables this for the investigation of new compounds in diseases involving demyelination.

A population analysis of the DGAT1 inhibitor GSK3008356 and its effect on endogenous and meal-induced triglyceride turnover in healthy subjects.

Non-alcoholic steatohepatitis (NASH) is a liver disease in which fatty infiltration is accompanied by liver inflammation. GSK3008356 is under development as a selective inhibitor of diacylglycerol acyltransferase 1 (DGAT1), a key enzyme involved in the formation of triglyceride (TG). Decreased DGAT1 activity can reduce circulating TG and liver TG, and therefore could potentially prevent or treat NASH. The aim of the current study was to develop a population pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the PK disposition of GSK3008356 and its relation to the changes in blood TG. Drug concentrations were measured in 104 healthy adults receiving various single (SD) and repeat doses (RD) in a first time in human (FiH) study. A 30% fat meal was given at hour 2 postdose, and blood postprandial TG concentrations were measured at various time points. The population PKPD model consists of several parts including a PK model, drug effect model, meal effect model, and a turnover model. The pharmacokinetic data were described using a 3-compartment model. Drug effect was described by an inhibitory sigmoidal Emax model. Since TG levels change with the introduction of a meal, a bi-exponential meal effect model was utilized. The total change in TG was fitted using a turnover model with drug and meal effects on the TG production rate. The current analysis presents a PKPD modeling strategy of time-varying TG data coming from both endogenous and exogenous sources. In general, the presented model could be utilized in the model-based drug development of drugs that influence TG levels in blood.

Power Determination During Drug Development: Is Optimizing the Sample Size Based on Exposure-Response Analyses Underutilized?

The use of model‐based drug development (MBDD) has been demonstrated to improve the efficiency of clinical trial design. However, MBDD complexity can limit its use, particularly early in clinical development. In this tutorial, a simple and generalizable exposure‐response analysis approach to determine the power for dose‐ranging studies is presented and described. We identified situations in which higher power and sample size reduction is achieved by utilizing the exposure‐response powering methodology compared with conventional power calculations.

Pharmacokinetics, Pharmacodynamics, and Safety of E6011, a Novel Humanized Antifractalkine (CX3CL1) Monoclonal Antibody: A Randomized, Double-Blind, Placebo-Controlled Single-Ascending-Dose Study.

E6011 is a novel humanized antifractalkine (FKN) monoclonal antibody being developed as a therapeutic target for Crohn's disease, rheumatoid arthritis, and primary biliary cholangitis. This study was a randomized, double-blind, placebo-controlled single-ascending-dose study of intravenous administration of E6011 (0.0006-10mg/kg) in healthy Japanese adult men (n = 64). The starting dose was the minimum anticipated biological effect level (MABEL). MABEL was estimated by extrapolating results of a pharmacokinetic/pharmacodynamic (PK/PD) model relating E6011 exposure and suppression of free soluble FKN using data obtained from cynomolgus monkeys. Safety assessments consisted of monitoring and recording adverse events, laboratory tests, vital signs, intensive electrocardiograms, and chest x-rays. Blood samples to determine PK, PD (serum total FKN concentration), and serum anti-E6011 antibody were collected. Noncompartmental analysis was used to derive PK parameters. Single intravenous infusions of E6011 were safe and well tolerated in healthy subjects. Serum E6011 concentrations showed triphasic elimination. An increase in serum total FKN concentration was observed, confirming target engagement. The dose strategy for patient studies is to select regimens that will attain a minimum serum E6011 exposure of 10 μg/mL, identified as the minimum concentration needed to saturate the target-mediated elimination pathway. Model-based drug development from preclinical stage was successful in identifying dose regimens for clinical testing.

Abstract 1037: Comprehensive molecular profiles of gastric cancer patient derived xenograft (PDX) models and its implication in precision cancer medicine

Abstract Background: Recent cancer research is focused on precision medicine with the advent of next generation sequencing (NGS) technology and patient tumor derived model systems. Here, we present molecular characteristics of gastric cancer patient derived xenograft (PDX) models and explore the potential of molecularly defined PDX model based drug development. Materials and Methods: We generated PDX models from patient tumors with advanced gastric cancer. The genomic alterations of tumors were profiled by whole exome sequencing (WES), RNA sequencing (RNAseq), targeted sequencing, in-situ hybridization (ISH) and immunohistochemistry (IHC). Further, we developed overcoming strategy of chemotherapy resistance mechanism by combination of signaling pathway inhibitor and standard chemotherapy regimen in “N-of-1” PDX trial. Results: Thirty-five PDX models were successfully established and categorized into four subgroups of The Cancer Genome Atlas (TCGA) and Asian Cancer Research Group (ACRG) classifications: EBV (2.9%), MSI (20.6%), GS (23.5%) and CIN (52.9%) subtypes by TCGA while MSI type (11.4%), MSS/EMT type (17.1%), MSS/TP53+ (31.4%) and MSS/TP53- type (40.0%) by ACRG. In the protein levels by IHC, there were 21 cases (60.0%) of any RTK proteins overexpression; nine of HER2 (25.7%), 14 of EGFR (40.0%), and 16 of c-MET (45.7%). Five tumors (14.3%) were related to PTEN loss and 22 tumors (62.9%) showed p53 overexpression or null. Targeted sequencing identified that ERBB2 (25.7%), KRAS (11.4%), and CCND1 (11.4%) were found frequently amplified gene while PIK3CA (11.4%) and CTNNB1 (8.6%) were found most mutated genes. N-of-1 PDX trial demonstrated that the response to FOLFOX in PDX tumor was concordant with that of corresponding patient. In FOLFOX resistant tumors, multiple signaling pathways were up-regulated and inhibition of these signaling pathways was regressed tumor growth. Conclusions: The utilization of molecularly catalogued gastric cancer PDX models will guide precision medicine for cancer therapy and be a useful tool for drug development and repurposing. Citation Format: Jae Eun Lee, Yoon Young Choi, Ju Yeon Lim, Su-Jin Shin, Gunho Lee, Eun Young Kim, Taeil Son, Hyoung-Il Kim, Woo Jin Hyung, Sung Hoon Noh, Hyunki Kim, Minkyu Jung, Sangwoo Kim, Soonmyung Paik, Jae-Ho Cheong. Comprehensive molecular profiles of gastric cancer patient derived xenograft (PDX) models and its implication in precision cancer medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1037.

Model-based drug development: application of modeling and simulation in drug development

Despite enormous success in biomedical science and technology, as well as increased research and development spending, pharmaceutical productivity has faced challenges. The success rates in drug development remain low and have shown a declining trend in the last two decades. The US FDA has also recognized the inefficiency in drug development and proposed model-based drug development (MBDD) to improve pharmaceutical productivity and decision making. Modeling and simulation provide a powerful tool to summarize and integrate information from different studies. Application of modeling and simulation can help decision making, design better studies, reduce costs, save time, and ultimately improve success rates. Beyond traditional types of modeling techniques or applications, MBDD is a paradigm that covers the entire spectrum of the drug development process. This review aims to provide an overview of modeling and simulation and their application to various drug development processes, from early discovery to preclinical and clinical stages, as well as formulation optimization. Several types of models will be discussed, and illustrative examples of their applications in the drug development process will be highlighted.

Clinical Pharmacology Tools and Evaluations to Facilitate Comprehensive Dose Finding in Oncology: A Continuous Risk-Benefit Approach.

Targeted therapies are now considered an integral component in the treatment armamentarium for many malignancies, and the approach to developing these drugs needs to be refined from the previous cytotoxic paradigm of toxicity-guided dose finding and identification of maximum tolerated dose to a paradigm driven by target activity. Moving away from the toxicity-driven dose finding and justification model requires an integrated approach in order to adequately characterize the risk-benefit of a drug. This approach starts with understanding the importance of collecting samples for pharmacokinetic and pharmacodynamic assessments in all phases of clinical development to fully characterize the pharmacokinetics and identify covariates and then correlating exposure to key markers of safety and efficacy in pharmacometric analyses to perform a robust risk-benefit assessment and establish the right dose. In addition, for oral agents, decisions on administering the drug with respect to food can impact dose among other clinical trial outcomes such as tolerability and patient compliance. Understanding the importance of model-based drug development as a decision-making tool to support drug development through incorporation of all relevant data allows for a robust risk-benefit assessment at key decision points. Utilization of clinical pharmacology tools and assessments throughout development will provide the key components of a successful oncology development program.

A systems biology analysis connects insulin receptor signaling with glucose transporter translocation in rat adipocytes

Type 2 diabetes is characterized by insulin resistance, which arises from malfunctions in the intracellular insulin signaling network. Knowledge of the insulin signaling network is fragmented, and because of the complexity of this network, little consensus has emerged for the structure and importance of the different branches of the network. To help overcome this complexity, systems biology mathematical models have been generated for predicting both the activation of the insulin receptor (IR) and the redistribution of glucose transporter 4 (GLUT4) to the plasma membrane. Although the insulin signal transduction between IR and GLUT4 has been thoroughly studied with modeling and time-resolved data in human cells, comparable analyses in cells from commonly used model organisms such as rats and mice are lacking. Here, we combined existing data and models for rat adipocytes with new data collected for the signaling network between IR and GLUT4 to create a model also for their interconnections. To describe all data (>140 data points), the model needed three distinct pathways from IR to GLUT4: (i) via protein kinase B (PKB) and Akt substrate of 160 kDa (AS160), (ii) via an AS160-independent pathway from PKB, and (iii) via an additional pathway from IR, e.g. affecting the membrane constitution. The developed combined model could describe data not used for training the model and was used to generate predictions of the relative contributions of the pathways from IR to translocation of GLUT4. The combined model provides a systems-level understanding of insulin signaling in rat adipocytes, which, when combined with corresponding models for human adipocytes, may contribute to model-based drug development for diabetes.

Clinical Pharmacology and Translational Aspects of Bispecific Antibodies.

Clinical Pharmacology and Translational Aspects of Bispecific Antibodies.