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Precision medicine aims to use patient genomic, epigenomic, specific drug dose, and other data to define disease patterns that may potentially lead to an improved treatment outcome. Personalized dosing regimens based on tumor drug penetration can play a critical role in this approach. State-of-the-art techniques to measure tumor drug penetration focus on systemic exposure, tissue penetration, cellular or molecular engagement, and expression of pharmacological activity. Using in silico methods, this information can be integrated to bridge the gap between the therapeutic regimen and the pharmacological link with clinical outcome. These methodologies are described, and challenges ahead are discussed. Supported by many examples, this review shows how the combination of these techniques provides enhanced patient-specific information on drug accessibility at the tumor tissue level, target binding, and downstream pharmacology. Our vision of how to apply tumor drug penetration measurements offers a roadmap for the clinical implementation of precision dosing.

Background and Purpose: Cotadutide is a dual GLP‐1 and glucagon receptor agonist with balanced agonistic activity at each receptor designed to harness the advantages on promoting liver health, weight loss and glycaemic control. We characterised the effects of cotadutide on glucose, insulin, GLP‐1, GIP, and glucagon over time in a quantitative manner using our glucose dynamics systems model (4GI systems model), in combination with clinical data from a multiple ascending dose/Phase 2a (MAD/Ph2a) study in overweight and obese subjects with a history of Type 2 diabetes mellitus (NCT02548585). Experimental Approach: The cotadutide PK‐4GI systems model was calibrated to clinical data by re‐estimating only food related parameters. In vivo cotadutide efficacy was scaled based on in vitro potency. The model was used to explore the effect of weight loss on insulin sensitivity and predict the relative contribution of the GLP‐1 and glucagon receptor agonistic effects on glucose. Key Results: Cotadutide MAD/Ph2a clinical endpoints were successfully predicted. The 4GI model captured a positive effect of weight loss on insulin sensitivity and showed that the stimulating effect of glucagon on glucose production counteracts the GLP‐1 receptor‐mediated decrease in glucose, resulting in a plateau for glucose decrease around a 200‐μg cotadutide dose. Conclusion and Implications: The 4GI quantitative systems pharmacology model was able to predict the clinical effects of cotadutide on glucose, insulin, GLP‐1, glucagon and GIP given known in vitro potency. The analyses demonstrated that the quantitative systems pharmacology model, and its successive refinements, will be a valuable tool to support the clinical development of cotadutide and related compounds. [ABSTRACT FROM AUTHOR]

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Abstract Background The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. Results A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. Conclusions The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.

Population approaches, traditionally employed in pharmacokinetic-pharmacodynamic studies, have shown value also in the context of glucose-insulin metabolism models by providing more accurate individual parameters estimates and a compelling statistical framework for the analysis of between-subject variability (BSV). In this work, the advantages of population techniques are further explored by proposing integration of covariates in the intravenous glucose tolerance test (IVGTT) glucose minimal model analysis. A previously published dataset of 204 healthy subjects, who underwent insulin-modified IVGTTs, was analyzed in NONMEM, and relevant demographic information about each subject was employed to explain part of the BSV observed in parameter values. Demographic data included height, weight, sex, and age, but also basal glycemia and insulinemia, and information about amount and distribution of body fat. On the basis of nonlinear mixed-effects modeling, age, visceral abdominal fat, and basal insulinemia were significant predictors for SI (insulin sensitivity), whereas only age and basal insulinemia were significant for P2 (insulin action). The volume of distribution correlated with sex, age, percentage of total body fat, and basal glycemia, whereas no significant covariate was detected to explain variability in SG (glucose effectiveness). The introduction of covariates resulted in a significant shrinking of the unexplained BSV, especially for SI and P2 and considerably improved the model fit. These results offer a starting point for speculation about the physiological meaning of the relationships detected and pave the way for the design of less invasive and less expensive protocols for epidemiological studies of glucose-insulin metabolism. NONMEM; population modeling; nonlinear mixed-effects modeling; insulin sensitivity; glucose metabolism doi: 10.1152/ajpendo.00656.2009.

We describe a method to measure protein synthesis and catabolism in humans without prior purification and use the method to measure the turnover of surfactant protein-B (SP-B). SP-B, a lung-specific, hydrophobic protein essential for fetal neonatal respiratory transition, is present in only picomolar quantities in tracheal aspirate samples and difficult to isolate for dynamic turnover studies using traditional in vivo tracer techniques. Using infusion of [5,5,5-[sup.2][H.sub.3]] leucine and a targeted proteomics method, we measured both the quantity and kinetics of SP-B tryptic peptides in tracheal aspirate samples of symptomatic newborn infants. The fractional synthetic rate (FSR) of SP-B measured using the most abundant proteolytic fragment, a 10 amino acid peptide from the carboxy-terminus of proSP-B (SPTGEWLPR), from the circulating leucine pool was 0.035 [+ or -] 0.005 [h.sup.-l], and the fractional catabolic rate was 0.044 [+ or -] 0.003 h This technique permits high-throughput and sensitive measurement of turnover of low abundance proteins with minimal sample preparation. 10.1021/ac1001433

The intravenous glucose tolerance test (IVGTT) interpreted with the minimal model provides individual indexes of insulin sensitivity ([S.sub.I]) and glucose effectiveness ([S.sub.G]). In population studies, the traditional approach, the standard two-stage (STS) method, fails to account for uncertainty in individual estimates, resulting in an overestimation of between-subject variability. Furthermore, in the presence of reduced sampling and/or insulin resistance, individual estimates may be unobtainable, biasing population information. Therefore, we investigated the use of two population approaches, the iterative two-stage (ITS) method and nonlinear mixed-effects modeling (NM), in a population (n = 235) of insulin-sensitive and insulin-resistant subjects under full (FSS, 33 samples) and reduced [RSS(240-min), 13 samples and RSS(180-min), 12 samples] IVGTT sampling schedules. All three population methods gave similar results with the FSS. Using RSS(240), the three methods gave similar results for [S.sub.I], but [S.sub.G] population means were overestimated. With RSS(180), [S.sub.I] and [S.sub.G] population means were higher for all three methods compared with their FSS counterparts. NM estimated similar between-subject variability (19% So, 53% SI) with RSS(180), whereas ITS showed regression to the mean for [S.sub.G] (0.01% [S.sub.G], 56% [S.sub.I]) and STS provided larger population variability in [S.sub.I] (29% [S.sub.G], 91% [S.sub.I]). NM provided individual estimates for all subjects, whereas the two-stage methods failed in 16-18% of the subjects using RSS(180) and 6-14% using RSS(240). We conclude that population approaches, specifically NM, are useful in studies with a sparsely sampled IVGTT (~12 samples) of short duration (~3 h) and when individual parameter estimates in all subjects are desired. NONMEM; two-stage; parameter estimation; minimal model; insulin sensitivity

We have developed a new model to describe endogenous glucose kinetics during a labeled (hot) intravenous glucose tolerance test (IVGTT) to derive a time profile of endogenous glucose production (EGP). We reanalyzed data from a previously published study (P. Vicini, J. J. Zachwieja, K. E. Yarasheski, D. M. Bier, A. Caumo, and C. Cobelli. Am J Physiol Endocrinol Metab 276: E285-E294, 1999), in which insulin-modified [6,6-[sup.2][H.sub.2]]glucose-labeled IVGTTs (0.33 g/kg glucose) were performed in 10 normal subjects. In addition, a second tracer ([U-[sup.13]C]glucose) was infused in a variable rate to clamp the endogenous glucose tracer-to-tracee ratio (TTR). Our new model describing endogenous glucose kinetics was incorporated into the two-compartment hot minimal-model structure. The model gave estimates of glucose effectiveness [1.54 [+ or -] 0.31 (SE) ml x [kg.sup.-1] x [min.sup.-1], insulin sensitivity (37.74 [+ or -] 5.23 [10.sup.4] dl x [kg.sup.-1] x [min.sup.-1] x [micro][U.sup.-1] x [min.sup.-1]], and a new parameter describing the sensitivity of EGP to the inhibitory effect of insulin (I[C.sub.50] = 0.0195 [+ or -] 0.0046 [min.sup.-1]). The model additionally provided an estimate of the time course of EGP showing almost immediate inhibition, followed by a secondary inhibitory effect caused by infusion of insulin, and a large overshoot as EGP returns to its basal value. Our estimates show very good agreement with those obtained via deconvolution and the model-independent TTR clamp technique. These results suggest that the new integrated model can serve as a simple one-step approach to obtain metabolic indexes while also providing a parametric description of EGP. hot minimal model; intravenous glucose tolerance test; mathematical modeling; insulin sensitivity