BackgroundNumerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies.MethodsA quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx.ResultsThe model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1.ConclusionsThis study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0327-9) contains supplementary material, which is available to authorized users.

Copper transport by the P 1 -ATPase ATP7B, or Wilson disease protein (WNDP), 1 is essential for human metabolism. Perturbation of WNDP function causes intracellular copper accumulation and severe pathology, known as Wilson disease (WD). Several WD mutations are clustered within the WNDP nucleotide-binding domain (Ndomain), where they are predicted to disrupt ATP binding. The mechanism by which the N-domain coordinates ATP is presently unknown, because residues important for nucleotide binding in the better characterized P 2 -ATPases are not conserved within the P 1 -ATPase subfamily. To gain insight into nucleotide binding under normal and disease conditions, we generated the recombinant WNDP N-domain and several WD mutants. Using isothermal titration calorimetry, we demonstrate that the N-domain binds ATP in a Mg 2؉ -independent manner with a relatively high affinity of 75 M, compared with millimolar affinities observed for the P 2 -ATPase N-domains. The WNDP N-domain shows minimal discrimination between ATP, ADP, and AMP, yet discriminates well between ATP and GTP. Similar results were obtained for the N-domain of ATP7A, another P 1 -ATPase. Mutations of the invariant WNDP residues E1064A and H1069Q drastically reduce nucleotide affinities, pointing to the likely role of these residues in nucleotide coordination. In contrast, the R1151H mutant exhibits only a 1.3-fold reduction in affinity for ATP. The C1104F mutation significantly alters protein folding, whereas C1104A does not affect the structure or function of the N-domain. Together, the results directly demonstrate the phenotypic diversity of WD mutations within the N-domain and indicate that the nucleotide-binding properties of the P 1 -ATPases are distinct from those of the P 2 -ATPases.The Wilson disease protein (WNDP) is a key regulator of copper homeostasis in a number of tissues, particularly the liver, brain, and kidneys (1, 2). WNDP transports copper from cytosol across cell membranes, using the energy of ATP hydrolysis. Under basal conditions, WNDP delivers copper to enzymes within the secretory pathway; it is also essential for cellular copper excretion when copper concentrations are elevated (1-3). Mutations in WNDP result in marked accumulation of copper in the cytosol and a severe hepatoneurological disorder known as Wilson disease (WD) (4, 5). The clinical manifestations of WD are diverse (6); however, the specific contributions of various WD mutations to phenotypic diversity remain poorly understood (7). Elucidating the consequences of mutations on WNDP structure and function is the first step toward a better understanding of molecular mechanisms underlying WD. In addition, an intriguing connection has recently been made between overexpression of WNDP and increased resistance of cells to the anticancer drug cisplatin (8 -10). These findings point to a role for WNDP as a potential pharmacological target and further emphasize the need for a better understanding of the protein's structure, function, and regulation. At present, such structural ...

BackgroundIntestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.Methodology/Principal FindingsIn this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.Conclusion/SignificanceThe model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

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