Odronextamab is a fully‐human IgG4‐based CD20xCD3 bispecific antibody that binds to CD3 on T cells and CD20 on B cells, triggering T‐cell‐mediated cytotoxicity independent of T‐cell‐receptor ...recognition. Adequate safety, tolerability, and encouraging durable complete responses have been observed in an ongoing first‐in‐human (FIH) study of odronextamab in patients with relapsed/refractory (R/R) B‐cell non‐Hodgkin lymphoma (B‐NHL; NCT02290951). We retrospectively evaluated the pharmacokinetic, pharmacodynamic, and antitumor characteristics of odronextamab in a series of in vitro/in vivo preclinical experiments, to assess their translational value to inform dose escalation for the FIH study. Half‐maximal effective concentration values from in vitro cytokine release assays (range: 0.05–0.08 mg/L) provided a reasonable estimate of odronextamab concentrations in patients associated with cytokine release at a 0.5 mg dose (maximum serum concentration: 0.081 mg/L) on week 1/day 1, which could therefore be used to determine the week 1 clinical dose. Odronextamab concentrations resulting in 100% inhibition of tumor growth in a Raji xenograft tumor mouse model (1–10 mg/L) were useful to predict efficacious concentrations in patients and inform dose‐escalation strategy. Although predicted human pharmacokinetic parameters derived from monkey data overestimated projected odronextamab exposure, they provided a conservative estimate for FIH starting doses. With step‐up dosing, the highest‐tested weekly odronextamab dose in patients (320 mg) exceeded the 1 mg/kg single dose in monkeys without step‐up dosing. In conclusion, combination of odronextamab in vitro cytokine data, efficacious concentration data from mouse tumor models, and pharmacokinetic evaluations in monkeys has translational value to inform odronextamab FIH study design in patients with R/R B‐NHL.
A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin‐like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative ...systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)‐rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism. Modifications included the addition of intermediate‐density lipoprotein and low‐density lipoprotein compartments to address the modulation of lipoprotein lipase (LPL) activity by evinacumab, ANGPTL3 biosynthesis and clearance, and a target‐mediated drug disposition model. A sensitivity analysis guided the creation of virtual patients (VPs). The drug‐free QSP model was found to agree well with clinical data published with the initial hepatic liver model over simulations ranging from 20 to 365 days in duration. The QSP model, including the interaction between LPL and ANGPTL3, was validated against clinical data for total evinacumab, total ANGPTL3, and TG concentrations as well as inhibition of apolipoprotein CIII. Free ANGPTL3 concentration and LPL activity were also modeled. In total, seven VPs were created; the lipid levels of the VPs were found to match the range of responses observed in evinacumab clinical trial data. The QSP model results agreed with clinical data for various subjects and was shown to characterize known TG physiology and drug effects in a range of patient populations with varying levels of TGs, enabling hypothesis testing of evinacumab effects on lipid metabolism.
Microalgae are microscopic plants that exist in an aquatic environment. They are involved in the production of high value compounds and also have applications in energy production. In this work, the ...regulation of biomass concentration in a bioreactor is investigated by using an observer-based backstepping control approach. The process is controlled to operate in a constant biomass concentration mode, in order to maintain the culture at a desired concentration and to sustain high biomass production levels. Combined with the backstepping controller, a nonlinear Lipschitz observer is proposed. On the basis of the Droop model, which describes the dynamic behavior of the microalgae process and based on the biomass measurements, the stability of the state error dynamics can be guaranteed. Finally, simulations and comparisons with a PI controller are developed to show the performance of the nonlinear observer-based controller in set point tracking and load rejection in the presence of parameter uncertainties.
REGN‐EB3 (Inmazeb) is a cocktail of three human monoclonal antibodies approved for treatment of Ebola infection. This paper describes development of a mathematical model linking REGN‐EB3’s inhibition ...of Ebola virus to survival in a non‐human primate (NHP) model, and translational scaling to predict survival in humans. Pharmacokinetic/pharmacodynamic data from single‐ and multiple‐dose REGN‐EB3 studies in infected rhesus macaques were incorporated. Using discrete indirect response models, the antiviral mechanism of action was used as a forcing function to drive the reversal of key Ebola disease hallmarks over time, for example, liver and kidney damage (elevated alanine ALT and aspartate aminotransferases AST, blood urea nitrogen BUN, and creatinine), and hemorrhage (decreased platelet count). A composite disease characteristic function was introduced to describe disease severity and integrated with the ordinary differential equations estimating the time course of clinical biomarkers. Model simulation results appropriately represented the concentration‐dependence of the magnitude and time course of Ebola infection (viral and pathophysiological), including time course of viral load, ALT and AST elevations, platelet count, creatinine, and BUN. The model estimated the observed survival rate in rhesus macaques and the dose of REGN‐EB3 required for saturation of the pharmacodynamic effects of viral inhibition, reversal of Ebola pathophysiology, and survival. The model also predicted survival in clinical trials with appropriate scaling to humans. This mathematical investigation demonstrates that drug‐disease modeling can be an important translational tool to integrate preclinical data from an NHP model recapitulating disease progression to guide future translation of preclinical data to clinical study design.
Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. ...Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.
Precision medicine (PM) refers to the use of available genomic information from an individual patient to select the most appropriate therapy for a disease. In this paper, we have developed a ...mechanistic multiscale modeling framework for the whole human body integrated with human hepatocyte genomic data. The model is validated by estimating the concentrations of several biomarkers in different amino acid inborn errors of metabolism (IEMs). To demonstrate the potential application of our multiscale modeling framework to precision medicine, we present a computational study of a specific disease. In this study, a genetic deficiency called Kelley–Seegmiller syndrome (KSS) is simulated for eight adult patients. Using our approach, we estimate the proper dosage of the drug for each subject needed to prevent hyperuricemia. These in silico results demonstrate that there is a significant difference in the optimal dose of the drug among individuals. In addition, the required dosages for the second and third days are different from those for the first day of treatment in each patient. Both results have important implications in terms of drug efficacy, drug side effects, and cost of the treatment. We also compare the pharmacological effect of available commercial drug tablets with these optimal values on disease progression. The results in this paper highlight the potential of the proposed modeling framework in opening up new opportunities in systems pharmacology and personalized medicine.
Cardiovascular disease (CVD) is the leading cause of death worldwide. Studies have found that abnormally high low-density lipoprotein cholesterol (LDL-C) levels are the highest risk factor for ...occurrences of CVD. In this paper, we have developed a mathematical model for LDL-C regulation in the human body. A multi-scale modeling approach has been used to integrate cholesterol synthesis in the human liver with diet cholesterol, LDL receptor trafficking pathways, and proprotein convertase subtilisin/kexin type 9 (PCSK9) function. Dynamic flux balance analysis (dFBA) has been used to integrate the hepatocyte genome-scale metabolic model with the multi-scale model of the LDL-C regulation. In this approach, the hepatocyte genome-scale model has been used to calculate the synthesis of the cholesterol. The resulting estimation has been utilized to estimate VLDL, LDL-C and other lipoproteins. In addition, LDL-C receptor signaling pathway has been integrated to estimate the LDL-C uptake rate in the liver cell. An in silico study has been carried out to demonstrate the potential application of this modelling framework to quantitative systems pharmacology (QSP). In this study, we have created a virtual subject with high levels of LDL-C as representative of a population with high levels of PCSK9 and liver cholesterol synthesis rates. Statin and anti-PCSK9 pharmacodynamic and pharmacokinetic (PD/PK) models have been integrated with the proposed network to estimate LDL-C reduction after drug administration. The simulation results indicate that combination therapy is necessary to reduce the LDL-C levels for this patient. These results are consistent with experimental evidence showing that the low-dose combination therapy may be the best approach to achieve the recommended LDL-C levels for patients with multiple risk factors for coronary heart disease. This novel modelling framework has great potential in quantitative systems pharmacology to improve decision making, reduce the risk of treatment failure, and improve dose selection associated with the LDL-C lowering therapy.
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