Modeling and simulation in clinical pharmacology and dose finding.
Ontology highlight
ABSTRACT:
SUBMITTER: Staab A
PROVIDER: S-EPMC3600758 | biostudies-other | 2013
REPOSITORIES: biostudies-other
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Project description:Avadomide is a cereblon E3 ligase modulator and a potent antitumor and immunomodulatory agent. Avadomide trials are challenged by neutropenia as a major adverse event and a dose-limiting toxicity. Intermittent dosing schedules supported by preclinical data provide a strategy to reduce frequency and severity of neutropenia; however, the identification of optimal dosing schedules remains a clinical challenge. Quantitative systems pharmacology (QSP) modeling offers opportunities for virtual screening of efficacy and toxicity levels produced by alternative dose and schedule regimens, thereby supporting decision-making in translational drug development. We formulated a QSP model to capture the mechanism of avadomide-induced neutropenia, which involves cereblon-mediated degradation of transcription factor Ikaros, resulting in a maturation block of the neutrophil lineage. The neutropenia model was integrated with avadomide-specific pharmacokinetic and pharmacodynamic models to capture dose-dependent effects. Additionally, we generated a disease-specific virtual patient population to represent the variability in patient characteristics and response to treatment observed for a diffuse large B-cell lymphoma trial cohort. Model utility was demonstrated by simulating the avadomide effect in the virtual population for various dosing schedules and determining the incidence of high-grade neutropenia, its duration, and the probability of recovery to low-grade neutropenia.
| S-EPMC8397660 | biostudies-literature
Project description:IntroductionEarly phase dose-finding (EPDF) studies are critical for the development of new treatments, directly influencing whether compounds or interventions can be investigated in further trials to confirm their safety and efficacy. There exists guidance for clinical trial protocols and reporting of completed trials in the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 and CONsolidated Standards Of Reporting Randomised Trials (CONSORT) 2010 statements. However, neither the original statements nor their extensions adequately cover the specific features of EPDF trials. The DEFINE (DosE-FIndiNg Extensions) study aims to enhance transparency, completeness, reproducibility and interpretation of EPDF trial protocols (SPIRIT-DEFINE) and their reports once completed (CONSORT-DEFINE), across all disease areas, building on the original SPIRIT 2013 and CONSORT 2010 statements.Methods and analysisA methodological review of published EPDF trials will be conducted to identify features and deficiencies in reporting and inform the initial generation of the candidate items. The early draft checklists will be enriched through a review of published and grey literature, real-world examples analysis, citation and reference searches and consultation with international experts, including regulators and journal editors. Development of CONSORT-DEFINE commenced in March 2021, followed by SPIRIT-DEFINE from January 2022. A modified Delphi process, involving worldwide, multidisciplinary and cross-sector key stakeholders, will be run to refine the checklists. An international consensus meeting in autumn 2022 will finalise the list of items to be included in both guidance extensions.Ethics and disseminationThis project was approved by ICR's Committee for Clinical Research. The Health Research Authority confirmed Research Ethics Approval is not required. The dissemination strategy aims to maximise guideline awareness and uptake, including but not limited to dissemination in stakeholder meetings, conferences, peer-reviewed publications and on the EQUATOR Network and DEFINE study websites.Registration detailsSPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network.
| S-EPMC10069529 | biostudies-literature
Project description:While a number of phase I dose-finding designs in oncology exist, the commonly used ones are either algorithmic or empirical model-based. We propose a new framework for modeling the dose-response relationship, by systematically incorporating the pharmacokinetic (PK) data collected in the trial and the hypothesized mechanisms of the drug effects, via dynamic PK/PD modeling, as well as modeling of the relationship between a latent cumulative pharmacologic effect and a binary toxicity outcome. This modeling framework naturally incorporates the information on the impact of dose, schedule and method of administration (e.g., drug formulation and route of administration) on toxicity. The resulting design is an extension of existing designs that make use of pre-specified summary PK information (such as the area under the concentration-time curve [AUC] or maximum serum concentration [Cmax ]). Our simulation studies show, with moderate departure from the hypothesized mechanisms of the drug action, that the performance of the proposed design on average improves upon those of the common designs, including the continual reassessment method (CRM), Bayesian optimal interval (BOIN) design, modified toxicity probability interval (mTPI) method, and a design called PKLOGIT that models the effect of the AUC on toxicity. In case of considerable departure from the underlying drug effect mechanism, the performance of the design is shown to be comparable with that of the other designs. We illustrate the proposed design by applying it to the setting of a phase I trial of a γ-secretase inhibitor in metastatic or locally advanced solid tumors. We also provide R code to implement the proposed design.
| S-EPMC10134386 | biostudies-literature
Project description:PurposeGarenoxacin, a novel des-F(6)-quinolone, possesses potent antibacterial activity against infectious pathogens in the respiratory tract. Population pharmacokinetic/pharmacodynamic (PK/PD) modeling and Monte Carlo simulations were used to optimize garenoxacin dosage regimens.MethodsAt the end of phase II stage, the clinical dose of garenoxacin was predicted to be 400 mg once daily by the interim PK/PD analysis using phase I and phase II clinical data. The criteria used to determine an optimal dose were (1) the target attainment of the area under the unbound concentration-time curve divided by the minimum inhibitory concentration (fAUC????/MIC ratio) and (2) the maintenance of a trough concentration above the mutant prevention concentration. In a confirmatory phase III study, garenoxacin was administered 400 mg once daily to 136 patients infected with mild or moderate chronic respiratory diseases.ResultsLogistic regression analysis showed that fAUC????/MIC ratio was a significant variable that predicted clinical response (p?=?0.0164). Of all subjects, 92.4% reached the target value of fAUC????/MIC ratio?>?30 h, and the clinical efficacy rate of this population was 91.8%. On the other hand, there was no significant relationship between exposure values (AUC???? and maximum concentration) and the incidence of adverse events by the Mann-Whitney test.ConclusionsThe antimicrobial efficacy of the actual phase III study was consistent with the expectation from the Monte Carlo PD simulation. We were able to show that the optimal garenoxacin dosage regimens were successfully determined using prospective population PK/PD analysis and clinical trial simulations.
| S-EPMC3249185 | biostudies-literature
Project description:BackgroundSAGE-217, a novel ?-aminobutyric acid A (GABAA) receptor positive allosteric modulator, was evaluated in phase I, double-blind, placebo-controlled, single ascending dose (SAD) and multiple ascending dose (MAD) studies to assess the safety and pharmacokinetics (PK) of SAGE-217 following administration as an oral solution.MethodsIn the SAD study, subjects were randomized 6:2 to a single dose of SAGE-217 or placebo. Doses ranged from 0.25 to 66 mg across nine cohorts. In the MAD study, subjects were randomized 9:3 and received SAGE-217 (15, 30, or 35 mg) or placebo once daily for 7 days. In both studies, PK, maximum tolerated dose (MTD; against predetermined criteria), safety, and tolerability were assessed.ResultsA total of 108 healthy volunteers enrolled in the studies-72 subjects in the SAD study and 36 subjects in the MAD study. SAGE-217 was orally bioavailable, with a terminal-phase half-life of 16-23 h and a tmax of approximately 1 h. The MTDs for the oral solution of SAGE-217 in the SAD and MAD studies were determined to be 55 and 30 mg daily, respectively. In both studies, SAGE-217 was generally well tolerated, and no serious adverse events (SAEs) were reported. Most AEs were mild, dose-dependent, transient, occurred around the tmax, and related to drug pharmacology.ConclusionsSAGE-217 was generally well tolerated, and its PK profile was well characterized. Based on this profile, SAGE-217 has been advanced into multiple phase II clinical programs and pivotal studies of major depressive disorder and postpartum depression.
| S-EPMC6994455 | biostudies-literature
Project description:Dose-finding methods aiming at identifying an optimal dose of a treatment with a given schedule may be at a risk of misidentifying the best treatment for patients. In this article we propose a phase I/II clinical trial design to find the optimal dose-schedule combination. We define schedule as the method and timing of administration of a given total dose in a treatment cycle. We propose a Bayesian dynamic model for the joint effects of dose and schedule. The proposed model allows us to borrow strength across dose-schedule combinations without making overly restrictive assumptions on the ordering pattern of the schedule effects. We develop a dose-schedule-finding algorithm to sequentially allocate patients to a desirable dose-schedule combination, and select an optimal combination at the end of the trial. We apply the proposed design to a phase I/II clinical trial of a ?-secretase inhibitor in patients with refractory metastatic or locally advanced solid tumours, and examine the operating characteristics of the design through simulations.
| S-EPMC4747255 | biostudies-other
Project description:The overall aim was to investigate the effects of low and high dose vitamin D supplementation on genome-wide gene expression and how this is modulated by genetic variation. We adopted a functional genomics approach to analyse study participants in the BEST-D clinical trial (placebo, low dose or high dose supplementation over a 12-month treatment period).
2018-04-21 | E-MTAB-6246 | biostudies-arrayexpress
Project description:The aim of phase I clinical trials is to obtain reliable information on safety, tolerability, pharmacokinetics (PK), and mechanism of action of drugs with the objective of determining the maximum tolerated dose (MTD). In most phase I studies, dose-finding and PK analysis are done separately and no attempt is made to combine them during dose allocation. In cases such as rare diseases, paediatrics, and studies in a biomarker-defined subgroup of a defined population, the available population size will limit the number of possible clinical trials that can be conducted. Combining dose-finding and PK analyses to allow better estimation of the dose-toxicity curve should then be considered. In this work, we propose, study, and compare methods to incorporate PK measures in the dose allocation process during a phase I clinical trial. These methods do this in different ways, including using PK observations as a covariate, as the dependent variable or in a hierarchical model. We conducted a large simulation study that showed that adding PK measurements as a covariate only does not improve the efficiency of dose-finding trials either in terms of the number of observed dose limiting toxicities or the probability of correct dose selection. However, incorporating PK measures does allow better estimation of the dose-toxicity curve while maintaining the performance in terms of MTD selection compared to dose-finding designs that do not incorporate PK information. In conclusion, using PK information in the dose allocation process enriches the knowledge of the dose-toxicity relationship, facilitating better dose recommendation for subsequent trials.
| S-EPMC5573988 | biostudies-literature
Project description:PurposeWe provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice.MethodsWe identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances.ResultsOn average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below).ConclusionThis review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.
| S-EPMC4121508 | biostudies-literature
Project description:Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which efficacy and toxicity monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a dose-finding design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships.
| S-EPMC3913186 | biostudies-literature