Effect of Body Weight and Age on the Pharmacokinetics of Dihydroartemisinin: Food and Drug Administration Basis for Dose Determination of Artesunate for Injection in Pediatric Patients With Severe Malaria.

For treatment of severe malaria, the World Health Organization recommends 3 mg/kg intravenous artesunate in pediatric patients weighing less than 20 kg. Here we describe the Food and Drug Administration's rationale for selecting 2.4 mg/kg in pediatric patients weighing less than 20 kg based on literature review and independent analyses.

Esomeprazole FDA Approval in Children With GERD: Exposure-Matching and Exposure-Response.

OBJECTIVES: Food and Drug Administration approval of proton-pump inhibitors for infantile gastroesophageal reflux disease has been limited by intrapatient variability in the clinical assessment of gastroesophageal reflux disease. For children 1 to 17 years old, extrapolating efficacy from adults for IV esomeprazole was accepted. The oral formulation was previously approved in children. Exposure-response and exposure matching analyses were sought to identify approvable pediatric doses. METHODS: Intragastric pH biomarker comparisons between children and adults were conducted. Pediatric doses were selected to match exposures in adults and were based on population pharmacokinetic (PK) modeling and simulations with pediatric esomeprazole data. Observed IV or oral esomeprazole PK data were available from 50 and 117 children, between birth and 17 years, respectively, and from 65 adults, between 20 and 48 years. A population PK model developed using these data was used to simulate steady-state esomeprazole exposures for children at different doses to match the observed exposures in adults. RESULTS: Exposure-response relationships of intragastric pH measures were similar between children and adults. The PK simulations identified a dosing regimen for children that results in comparable steady-state area under the curve to that observed after 20 mg in adults. For IV esomeprazole, increasing the infusion duration to 10 to 30 minutes in children achieves matching Cmax values with adults. CONCLUSIONS: The exposure-matching analysis permitted approval of an esomeprazole regimen not studied directly in clinical trials. Exposure-response for intragastric pH-permitted approval for the treatment of gastroesophageal reflux disease in children in whom it was not possible to evaluate the adult primary endpoint, mucosal healing assessed by endoscopy.

Role of Quantitative Clinical Pharmacology in Pediatric Approval and Labeling.

Dose selection is one of the key decisions made during drug development in pediatrics. There are regulatory initiatives that promote the use of model-based drug development in pediatrics. Pharmacometrics or quantitative clinical pharmacology enables development of models that can describe factors affecting pharmacokinetics and/or pharmacodynamics in pediatric patients. This manuscript describes some examples in which pharmacometric analysis was used to support approval and labeling in pediatrics. In particular, the role of pharmacokinetic (PK) comparison of pediatric PK to adults and utilization of dose/exposure-response analysis for dose selection are highlighted. Dose selection for esomeprazole in pediatrics was based on PK matching to adults, whereas for adalimumab, exposure-response, PK, efficacy, and safety data together were useful to recommend doses for pediatric Crohn's disease. For vigabatrin, demonstration of similar dose-response between pediatrics and adults allowed for selection of a pediatric dose. Based on model-based pharmacokinetic simulations and safety data from darunavir pediatric clinical studies with a twice-daily regimen, different once-daily dosing regimens for treatment-naïve human immunodeficiency virus 1-infected pediatric subjects 3 to <12 years of age were evaluated. The role of physiologically based pharmacokinetic modeling (PBPK) in predicting pediatric PK is rapidly evolving. However, regulatory review experiences and an understanding of the state of science indicate that there is a lack of established predictive performance of PBPK in pediatric PK prediction. Moving forward, pharmacometrics will continue to play a key role in pediatric drug development contributing toward decisions pertaining to dose selection, trial designs, and assessing disease similarity to adults to support extrapolation of efficacy.

Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group.

The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

Landscape of regulatory quantitative systems pharmacology submissions to the U.S. Food and Drug Administration: An update report.

The number of quantitative systems pharmacology (QSP) submissions to the U.S. Food and Drug Administration has continued to increase over the past decade. This report summarizes the landscape of QSP submissions as of December 2023. QSP was used to inform drug development across various therapeutic areas and throughout the drug development process of small molecular drugs and biologics and has facilitated dose finding, dose ranging, and dose optimization studies. Though the majority of QSP submissions (>66%) focused on drug effectiveness, QSP was also utilized to simulate drug safety including liver toxicity, risk of cytokine release syndrome (CRS), bone density, and others. This report also includes individual contexts of use from a handful of new drug applications (NDAs) and biologics license applications where QSP modeling was used to demonstrate the utility of QSP modeling in regulatory drug development. According to the models submitted in QSP submissions, an anonymous case was utilized to illustrate how QSP informed development of a bispecific monoclonal antibody with respect to CRS risk. QSP submissions for informing pediatric drug development were summarized along with highlights of a case in inborn errors of metabolism. Furthermore, simulations of response variability with QSP were described. In summary, QSP continues to play a role in informing drug development.

Model-Informed Approaches to Support Drug Development for Patients With Obesity: A Regulatory Perspective.

Obesity, which is defined as having a body mass index of 30 kg/m2 or greater, has been recognized as a serious health problem that increases the risk of many comorbidities (eg, heart disease, stroke, and diabetes) and mortality. The high prevalence of individuals who are classified as obese calls for additional considerations in clinical trial design. Nevertheless, gaining a comprehensive understanding of how obesity affects the pharmacokinetics (PK), pharmacodynamics (PD), and efficacy of drugs proves challenging, primarily as obese patients are seldom selected for enrollment at the early stages of drug development. Over the past decade, model-informed drug development (MIDD) approaches have been increasingly used in drug development programs for obesity and its related diseases as they use and integrate all available sources and knowledge to inform and facilitate clinical drug development. This review summarizes the impact of obesity on PK, PD, and the efficacy of drugs and, more importantly, provides an overview of the use of MIDD approaches in drug development and regulatory decision making for patients with obesity: estimating PK, PD, and efficacy in specific dosing scenarios, optimizing dose regimen, and providing evidence for seeking new indication(s). Recent review cases using MIDD approaches to support dose selection and provide confirmatory evidence for effectiveness for patients with obesity, including pediatric patients, are discussed. These examples demonstrate the promise of MIDD as a valuable tool in supporting clinical trial design during drug development and facilitating regulatory decision-making processes for the benefit of patients with obesity.

Whole Body PBPK Modeling of Remdesivir and Its Metabolites to Aid in Estimating Active Metabolite Exposure in the Lung and Liver in Patients With Organ Dysfunction.

Remdesivir (RDV) is the first drug approved by the US Food and Drug Administration (FDA) for the treatment of coronavirus disease 2019 (COVID-19) in certain patients requiring hospitalization. As a nucleoside analogue prodrug, RDV undergoes intracellular multistep activation to form its pharmacologically active species, GS-443902, which is not detectable in the plasma. A question arises that whether the observed plasma exposure of RDV and its metabolites would correlate with or be informative about the exposure of GS-443902 in tissues. A whole body physiologically-based pharmacokinetic (PBPK) modeling and simulation approach was utilized to elucidate the disposition mechanism of RDV and its metabolites in the lungs and liver and explore the relationship between plasma and tissue pharmacokinetics (PK) of RDV and its metabolites in healthy subjects. In addition, the potential alteration of plasma and tissue PK of RDV and its metabolites in patients with organ dysfunction was explored. Our simulation results indicated that intracellular exposure of GS-443902 was decreased in the liver and increased in the lungs in subjects with hepatic impairment relative to the subjects with normal liver function. In subjects with severe renal impairment, the exposure of GS-443902 in the liver was slightly increased, whereas the lung exposure of GS-443902 was not impacted. These predictions along with the organ impairment study results may be used to support decision making regarding the RDV dosage adjustment in these patient subgroups. The modeling exercise illustrated the potential of whole body PBPK modeling to aid in decision making for nucleotide analogue prodrugs, particularly when the active metabolite exposure in the target tissues is not available.

Model-Informed Drug Development Approaches to Assist New Drug Development in the COVID-19 Pandemic.

Leveraging limited clinical and nonclinical data through modeling approaches facilitates new drug development and regulatory decision making amid the coronavirus disease 2019 (COVID-19) pandemic. Model-informed drug development (MIDD) is an essential tool to integrate those data and generate evidence to (i) provide support for effectiveness in repurposed or new compounds to combat COVID-19 and dose selection when clinical data are lacking; (ii) assess efficacy under practical situations such as dose reduction to overcome supply issues or emergence of resistant variant strains; (iii) demonstrate applicability of MIDD for full extrapolation to adolescents and sometimes to young pediatric patients; and (iv) evaluate the appropriateness for prolonging a dosing interval to reduce the frequency of hospital visits during the pandemic. Ongoing research activities of MIDD reflect our continuous effort and commitment in bridging knowledge gaps that leads to the availability of effective treatments through innovation. Case examples are presented to illustrate how MIDD has been used in various stages of drug development and has the potential to inform regulatory decision making.

Quantitative systems pharmacology: Landscape analysis of regulatory submissions to the US Food and Drug Administration.

Quantitative systems pharmacology (QSP) has been proposed as a scientific domain that can enable efficient and informative drug development. During the past several years, there has been a notable increase in the number of regulatory submissions that contain QSP, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), and Biologics License Applications (BLAs) to the US Food and Drug Administration. However, there has been no comprehensive characterization of the nature of these regulatory submissions regarding model details and intended applications. To address this gap, a landscape analysis of all the QSP submissions as of December 2020 was conducted. This report summarizes the (1) yearly trend of submissions, (2) proportion of submissions between INDs and NDAs/BLAs, (3) percentage distribution along the stages of drug development, (4) percentage distribution across various therapeutic areas, and (5) nature of QSP applications. In brief, QSP is increasingly applied to model and simulate both drug effectiveness and safety throughout the drug development process across disease areas.

FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective.

The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).

Targeting 11q23 positive acute leukemia cells with high molecular weight-melanoma associated antigen-specific monoclonal antibodies.

BACKGROUND: Acute leukemia with 11q23 aberrations is associated with a poor outcome with therapy. The lack of efficacy of conventional therapy has stimulated interest in developing novel strategies. Recent studies have shown that 11q23-positive acute leukemia cells express the high molecular weight-melanoma associated antigen (HMW-MAA). This tumor antigen represents a useful target to control growth of human melanoma tumors in patients and in severe combined immunodeficient (SCID) mice, utilizing antibody-based immunotherapy. This effect appears to be mediated by inhibition of the HMW-MAA function such as triggering of the focal adhesion kinase/proline-rich tyrosine kinase 2 (Pyk2) pathways. Therefore, in this study we tested whether HMW-MAA-specific monoclonal antibodies (mAb) could inhibit growth of 11q23-positive leukemia cells in SCID mice. METHODS: HMW-MAA-specific mAb were tested for their ability to inhibit the in vitro proliferation of an 11q23-positive acute myeloid leukemia (AML) cell line and blasts from four patients with 11q23 aberrations and their in vivo growth in subcutaneous and disseminated xenograft models. RESULTS: The HMW-MAA-specific mAb did not affect in vitro proliferation although they down-regulated phosphorylated (P) Pyk2 expression. Furthermore, the mAb enhanced the in vitro anti-proliferative effect of cytarabine. In vivo the mAb inhibited the growth of leukemic cells in a dose-dependent fashion. However, the difference did not reach statistical significance. No effect was detected on P-Pyk2 expression. Furthermore, HMW-MAA-specific mAb in combination with cytarabine did not improve tumor inhibition. Lastly, the combination of two mAb which recognize distinct HMW-MAA determinants had no detectable effect on survival in a disseminated xenograft model. CONCLUSIONS: HMW-MAA-specific mAb down-regulated P-Pyk2 expression and enhanced the anti-proliferative effect of cytarabine in vitro, but had no detectable effect on survival or growth of leukemia cells in vivo. Whether the HMW-MAA-specific mAb can be used as carriers of toxins or chemotherapeutic agents against 11q23-acute leukemia remains to be determined.

Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices.

Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.

Modeling corticosteroid effects in a rat model of rheumatoid arthritis II: mechanistic pharmacodynamic model for dexamethasone effects in Lewis rats with collagen-induced arthritis.

A mechanism-based model for pharmacodynamic effects of dexamethasone (DEX) was incorporated into our model for arthritis disease progression in the rat to aid in identification of the primary factors responsible for edema and bone loss. Collagen-induced arthritis was produced in male Lewis rats after injection of type II porcine collagen. DEX was given subcutaneously in single doses of 0.225 or 2.25 mg/kg or 7-day multiple doses of 0.045 or 0.225 mg/kg at 21 days postdisease induction. Effects on disease progression were measured by paw swelling, bone mineral density (BMD), body weights, plasma corticosterone (CST), and tumor necrosis factor (TNF)-alpha, interleukin (IL)-1beta, IL-6, and glucocorticoid receptor (GR) mRNA expression in paw tissue. Lumbar and femur BMD was determined by PIXImus II dual-energy X-ray absorptiometry. Plasma CST was assayed by high-performance liquid chromatography. Cytokine and GR mRNA were assayed by quantitative real-time polymerase chain reaction. Indirect response models, drug interaction models, transduction processes, and the fifth-generation model of corticosteroid dynamics were integrated and applied using S-ADAPT software to describe how dexamethasone binding to GR can regulate diverse processes. Cytokine mRNA, GR mRNA, plasma CST, and paw edema were suppressed after DEX administration. TNF-alpha mRNA expression and BMD seemed to increase immediately after dosing but were ultimately reduced. Model parameters indicated that IL-6 and IL-1beta were most sensitive to inhibition by DEX. TNF-alpha seemed to primarily influence edema, whereas IL-6 contributed the most to bone loss. Lower doses of corticosteroids may be sufficient to suppress the cytokines most relevant to bone erosion.

Modeling corticosteroid effects in a rat model of rheumatoid arthritis I: mechanistic disease progression model for the time course of collagen-induced arthritis in Lewis rats.

A mechanism-based model was developed to describe the time course of arthritis progression in the rat. Arthritis was induced in male Lewis rats with type II porcine collagen into the base of the tail. Disease progression was monitored by paw swelling, bone mineral density (BMD), body weights, plasma corticosterone (CST) concentrations, and tumor necrosis factor (TNF)-alpha, interleukin (IL)-1beta, IL-6, and glucocorticoid receptor (GR) mRNA expression in paw tissue. Bone mineral density was determined by PIXImus II dual energy X-ray densitometry. Plasma CST was assayed by high-performance liquid chromatography. Cytokine and GR mRNA were determined by quantitative real-time polymerase chain reaction. Disease progression models were constructed from transduction and indirect response models and applied using S-ADAPT software. A delay in the onset of increased paw TNF-alpha and IL-6 mRNA concentrations was successfully characterized by simple transduction. This rise was closely followed by an up-regulation of GR mRNA and CST concentrations. Paw swelling and body weight responses peaked approximately 21 days after induction, whereas bone mineral density changes were greatest at 23 days after induction. After peak response, the time course in IL-1beta, IL-6 mRNA, and paw edema slowly declined toward a disease steady state. Model parameters indicate TNF-alpha and IL-1beta mRNA most significantly induce paw edema, whereas IL-6 mRNA exerted the most influence on BMD. The model for bone mineral density captures rates of turnover of cancellous and cortical bone and the fraction of each in the different regions analyzed. This small systems model integrates and quantitates multiple factors contributing to arthritis in rats.

Synergism between arsenic trioxide and heat shock protein 90 inhibitors on signal transducer and activator of transcription protein 3 activity--pharmacodynamic drug-drug interaction modeling.

PURPOSE: Constitutive signal transducer and activator of transcription 3 (STAT3) activity, observed in approximately 50% of acute myelogenous leukemia cases and associated with adverse treatment outcome, is down-regulated by arsenic trioxide (ATO). Heat shock protein (HSP) 90 is a molecular chaperone involved in signal transduction pathways. We hypothesized that HSP90 inhibitors will potentiate ATO effect on constitutive STAT3 activity and cell killing. One concern was that the effect of ATO and HSP90 inhibitors will result in up-regulation of HSP70, a protein known to inhibit apoptosis. EXPERIMENTAL DESIGN: We have used a semimechanistic pharmacodynamic model to characterize concentration-effect relationships of ATO and HSP90 inhibitors on constitutive STAT3 activity, HSP70 expression, and cell death in a cell line model. RESULTS: Pharmacodynamic interaction of ATO and three HSP90 inhibitors showed synergistic interactions in inhibiting constitutive STAT3 activity and inducing cell death, in spite of a concurrent synergistic up-regulation of HSP70. CONCLUSIONS: These preliminary results provide a basis for studying the combined role of ATO with HSP90 inhibitors in acute myelogenous leukemia with constitutive STAT3 activity.

The Utility of a Population Approach in Drug-Drug Interaction Assessments: A Simulation Evaluation.

This study aims at evaluating the utility of the population pharmacokinetics approach in therapeutic protein drug-drug-interaction (DDI) assessment. Simulations were conducted for 2 representative victim drugs, methotrexate and trastuzumab, using a parallel-group design with and without the interaction drug. The effect of a perpetrator on the exposure of the victim drug is described as the ratio of clearance/apparent clearance of the victim drug given with or without the perpetrator. The power of DDI assessment was calculated as the percentage of runs with 90% confidence interval of the estimated DDI effect within 80% to 125% for the scenarios of no DDI, benchmarked with the noncompartmental approach with intensive sampling. The impact of the number of subjects, the number of sampling points per subject, sampling time error, and model misspecification on the power of DDI determination were evaluated. Results showed that with equal numbers of subjects in each arm, the population pharmacokinetics approach with sparse sampling may need about the same or a higher number of subjects compared to a noncompartmental approach in order to achieve similar power. Increasing the number of subjects, even if only in the study drug alone arm, can increase the power. Sampling or dosing time error had notable impacts on the power for methotrexate but not for trastuzumab. Model misspecification had no notable impacts on the power for trastuzumab. Overall, the population pharmacokinetics approach with sparse sampling built in phase 2/3 studies allows appropriate DDI assessment with adequate study design and analysis and can be considered as an alternative to dedicated DDI studies.

Modeling effects of dexamethasone on disease progression of bone mineral density in collagen-induced arthritic rats.

A mechanism-based model was developed to characterize the crosstalk between proinflammatory cytokines, bone remodeling biomarkers, and bone mineral density (BMD) in collagen-induced arthritic (CIA) rats. Male Lewis rats were divided into five groups: healthy control, CIA control, CIA receiving single 0.225 mg kg(-1) subcutaneous (SC) dexamethasone (DEX), CIA receiving single 2.25 mg kg(-1) SC DEX, and CIA receiving chronic 0.225 mg kg(-1) SC DEX. The CIA rats underwent collagen induction at day 0 and DEX was injected at day 21 post-induction. Disease activity was monitored throughout the study and rats were sacrificed at different time points for blood and paw collection. Protein concentrations of interleukin (IL)-1ß, IL-6, receptor activator of nuclear factor kappa-B ligand (RANKL), osteoprotegerin (OPG), and tartrate-resistant acid phosphatase 5b (TRACP-5b) in paws were measured by enzyme-linked immunosorbent assays (ELISA). Disease progression and DEX pharmacodynamic profiles of IL-1ß, IL-6, RANKL, and OPG were fitted simultaneously and parameters were sequentially applied to fit the TRACP-5b and BMD data. The model was built according to the mechanisms reported in the literature and modeling was performed using ADAPT 5 software with naïve pooling. Time profiles of IL-1ß and IL-6 protein concentrations correlated with their mRNAs. The RANKL and OPG profiles matched previous findings in CIA rats. DEX inhibited the expressions of IL-1ß, IL-6, and RANKL, but did not alter OPG. TRACP-5b was also inhibited by DEX. Model predictions suggested that anti-IL-1ß therapy and anti-RANKL therapy would result in similar efficacy for prevention of bone loss among the cytokine antagonists.