Quantitative systems pharmacology (QSP) models are an important facet of pharmaceutical and clinical research as they combine mechanistic models of physiology in health and disease with ...pharmacokinetics/pharmacodynamics to predict systems-level effects. The quantitative clinical pharmacology toolbox has traditionally included both mechanistic modeling and population approaches, collectively called pharmacometrics, but the current landscape requires the optimization and use of multiple models together. Here, we explore several case studies in drug development that exemplify three approaches for using QSP and pharmacometrics models together - parallel synchronization, cross-informative use, and sequential integration. While these approaches are increasingly applied in drug development, achieving a true convergence of QSP and pharmacometrics that fully exploits their synergy will require new tools and methods that enable greater technical integration, in addition to nurturing scientists with diverse modeling expertise that enable cross-discipline strategy. Extensions of existing methods used in each approach as well as additional resources including machine learning models, data-at-scale, end-to-end computation platforms, and real-time analytics will enable this convergence.
Antibody (Ab) crosslinking of HLA I molecules on the surface of endothelial cells triggers proliferative and pro‐survival intracellular signaling, which is implicated in the process of chronic ...allograft rejection, also known as transplant vasculopathy (TV). The purpose of this study was to investigate the role of mammalian target of rapamycin (mTOR) in HLA I Ab‐induced signaling cascades. Everolimus provides a tool to establish how the mTOR signal network regulates HLA I–mediated migration, proliferation and survival. We found that everolimus inhibits mTOR complex 1 (mTORC1) by disassociating Raptor from mTOR, thereby preventing class I–induced phosphorylation of mTOR, p70S6K, S6RP and 4E‐BP1, and resultant class I–stimulated cell migration and proliferation. Furthermore, we found that everolimus inhibits class I–mediated mTORC2 activation (1) by disassociating Rictor and Sin1 from mTOR; (2) by preventing class I–stimulated Akt phosphorylation and (3) by preventing class I–mediated ERK phosphorylation. These results suggest that everolimus is more effective than sirolimus at antagonizing both mTORC1 and mTORC2, the latter of which is critical in endothelial cell functional changes leading to TV in solid organ transplantation after HLA I crosslinking. Our findings point to a potential therapeutic effect of everolimus in prevention of chronic Ab‐mediated rejection.
The authors investigate the effects of the mTOR inhibitor everolimus on HLA‐I antibody‐induced endothelial cell signaling, and find that everolimus inhibits HLA‐I antibody‐mediated formation of mTOR complexes, activation of mTORC1‐ and mTORC2‐related signaling networks, and functional changes more potently than sirolimus.
Disease progression modeling (DPM) represents an important model‐informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug ...development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open‐ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross‐functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read‐out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate‐quality data, collaborative regulatory guidance, and published examples of impact.
Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to real-world applications is that EAs usually need a large ...number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many real-world applications. Either an explicit fitness function does not exist, or the evaluation of the fitness is computationally very expensive. In both cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a comprehensive survey of the research on fitness approximation in evolutionary computation is presented. Main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed. To conclude, open questions and interesting issues in the field are discussed.
The fermentation of xylose is essential for the bioconversion of lignocellulose to fuels and chemicals, but wild-type strains of Saccharomyces cerevisiae do not metabolize xylose, so researchers have ...engineered xylose metabolism in this yeast. Glucose transporters mediate xylose uptake, but no transporter specific for xylose has yet been identified. Over-expressing genes for aldose (xylose) reductase, xylitol dehydrogenase and moderate levels of xylulokinase enable xylose assimilation and fermentation, but a balanced supply of NAD(P) and NAD(P)H must be maintained to avoid xylitol production. Reducing production of NADPH by blocking the oxidative pentose phosphate cycle can reduce xylitol formation, but this occurs at the expense of xylose assimilation. Respiration is critical for growth on xylose by both native xylose-fermenting yeasts and recombinant S, cerevisiae. Anaerobic growth by recombinant mutants has been reported. Reducing the respiration capacity of xylose-metabolizing yeasts increases ethanol production. Recently, two routes for arabinose metabolism have been engineered in S. cerevisiae and adapted strains of Pichia stipitis have been shown to ferment hydrolysates with ethanol yields of 0.45 g g(-1) sugar consumed, so commercialization seems feasible for some applications.
Despite considerable investment into potential therapeutic approaches for Alzheimer's disease (AD), currently approved treatment options are limited. Predictive modeling using quantitative systems ...pharmacology (QSP) can be used to guide the design of clinical trials in AD. This study developed a QSP model representing amyloid beta (Aβ) pathophysiology in AD. The model included mechanisms of Aβ monomer production and aggregation to form insoluble fibrils and plaques; the transport of soluble species between the compartments of brain, cerebrospinal fluid (CSF), and plasma; and the pharmacokinetics, transport, and binding of monoclonal antibodies to targets in the three compartments. Ordinary differential equations were used to describe these processes quantitatively. The model components were calibrated to data from the literature and internal studies, including quantitative data supporting the underlying AD biology and clinical data from clinical trials for anti‐Aβ monoclonal antibodies (mAbs) aducanumab, crenezumab, gantenerumab, and solanezumab. The model was developed for an apolipoprotein E (APOE) ɛ4 allele carrier and tested for an APOE ɛ4 noncarrier. Results indicate that the model is consistent with data on clinical Aβ accumulation in untreated individuals and those treated with monoclonal antibodies, capturing increases in Aβ load accurately. This model may be used to investigate additional AD mechanisms and their impact on biomarkers, as well as predict Aβ load at different dose levels for mAbs with known targets and binding affinities. This model may facilitate the design of scientifically enriched and efficient clinical trials by enabling a priori prediction of biomarker dynamics in the brain and CSF.
In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra‐patient ...heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter‐lesion variability accounted for 21% and 28% of the total variance in tumor shrinkage and treatment effect duration, respectively. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death compared to those located in the lung or the lymph nodes. Finally, we evaluated the utility of individual lesion follow‐up for dynamic predictions. Consistent with results at the population level, the individual lesion model outperformed a model relying only on SLD, especially at early landmark times and in patients with liver or bladder target lesions. Our results show that an individual lesion model can characterize the heterogeneity in tumor dynamics and its impact on survival in advanced cancer patients.
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