Crohn's disease (CD) is a complex inflammatory bowel disease whose pathogenesis appears to involve several immunologic defects causing functional impairment of the gut. Its complexity and the reported loss of effectiveness over time of standard of care together with the increase in its worldwide incidence require the application of techniques aiming to find new therapeutic strategies. Currently, systems pharmacology modeling has been gaining importance as it integrates the available knowledge of the system into a single computational model. In this work, the following workflow for robust application of systems pharmacology modeling was followed: 1) scope definition; 2) species selection and circulating plasma levels based on a search in the literature; 3) representation of model topology and parametrization of the interactions, after literature data extraction and curation, and the implementation of ordinary differential equations in SimBiology (MATLAB version R2018b); and 4) model curation and evaluation by visual comparison of simulated interleukin (IL) concentrations with the reported levels in plasma, and sensitivity analysis performed to confirm model robustness and identify the most influential parameters. Finally, 5) exposure to two dose levels of recombinant human IL10 was evaluated by simulation and comparison with reported clinical study results. In summary, we present a quantitative systems pharmacology model for the main ILs involved in CD developed using a standardized methodology and supported by a comprehensive repository summarizing the most relevant literature in the field. However, it has to be taken into account that external validation is still pending as available clinical data were primarily used for model training. SIGNIFICANCE STATEMENT Crohn's disease (CD) is a complex heterogeneous inflammatory bowel disorder. Systems pharmacology modeling offers a great opportunity for integration of the available knowledge on the disease using a computational framework. As a result of this work, a comprehensive repository along with a quantitative systems pharmacology model for the main interleukins involved in CD is provided. This model is useful for the in silico evaluation of biomarkers and potential therapeutic targets and can be adapted to address research gaps regarding CD.

Background and Purpose Acute intermittent porphyria (AIP) results from haplo-insufficiency of the porphobilinogen deaminase (PBGD) gene encoding the third enzyme in the haem biosynthesis pathway. As liver is the main organ of pathology for AIP, emerging therapies that restore enzyme hepatic levels are appealing. The objective of this work was to develop a mechanistic-based computational framework to describe the effects of novel PBGD mRNA therapy on the accumulation of neurotoxic haem precursors in small and large animal models. Experimental Approach Liver PBGD activity data and/or 24-hr urinary haem precursors were obtained from genetic AIP mice and wild-type mice, rats, rabbits, and macaques. To mimic acute attacks, porphyrogenic drugs were administered over one or multiple challenges, and animals were used as controls or treated with different PBGD mRNA products. Available experimental data were sequentially used to build and validate a semi-mechanistic mathematical model using non-linear mixed-effects approach. Key Results The developed framework accounts for the different biological processes involved (i.e., mRNA sequence, release from lipid nanoparticle and degradation, mRNA translation, increased PBGD activity in liver, and haem precursor metabolism) in a simplified mechanistic fashion. The model, validated using external data, shows robustness in the extrapolation of PBGD activity data in rat, rabbit, and non-human primate species. Conclusion and Implications This quantitative framework provides a valuable tool to compare PBGD mRNA drug products during early preclinical stages, optimize the amount of experimental data required, and project results to humans, thus supporting drug development and clinical dose and dosing regimen selection.

Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to determine differences in TS dynamics by using the ClassIfication Clustering of Individual Lesions (CICIL) methodology. Results from subgroup analyses comparing genetic mutations and TS metrics were assessed and applied to survival analyses. Data from four mCRC clinical studies were analyzed (1781 patients, 6369 iTLs). CICIL was used to assess differences in lesion TS dynamics within a tissue (intra-class) or across different tissues (inter-class). First, lesions were automatically classified based on their location. Cross-correlation coefficients (CCs) determined if each pair of lesions followed similar or opposite dynamics. Finally, CCs were grouped by using the K-means clustering method. Heterogeneity in tumor dynamics was lower in the intra-class analysis than in the inter-class analysis for patients receiving cetuximab. More tumor heterogeneity was found in KRAS mutated patients compared to KRAS wild-type (KRASwt) patients and when using sum of longest diameters versus sum of products of diameters. Tumor heterogeneity quantified as the median patient's CC was found to be a predictor of overall survival (OS) (HR = 1.44, 95% CI 1.08-1.92), especially in KRASwt patients. Intra- and inter-tumor tissue heterogeneities were assessed with CICIL. Derived metrics of heterogeneity were found to be a predictor of OS time. Considering differences between lesions' TS dynamics could improve oncology models in favor of a better prediction of OS.

Over the past decade, the insulin-like growth factor (IGF)-signaling pathway has gained substantial interest as potential therapeutic target in oncology. Xentuzumab, a humanized IgG1 monoclonal antibody, binds to IGF-I and IGF-II thereby inhibiting the downstream signaling essential for survival and tumor growth. This pathway is further regulated by circulating IGF binding proteins (IGFBPs). In this work, a mechanistic model characterizing the dynamics and interactions of IGFs, IGFBPs, and Xentuzumab has been developed to guide dose selection. Therefore, in vitro and in vivo literature information was combined with temporal IGF-I, IGF-II, and IGFBP-3 total plasma concentrations from two phase I studies. Based on the established quantitative framework, the time-course of free IGFs as ultimate drug targets not measured in clinics was predicted. Finally, a dose of 1000 mg/week-predicted to reduce free IGF-I and free IGF-II at steady-state by at least 90% and 64%, respectively-was suggested for phase II.

Context: Accurate hydrocortisone dosing in children with adrenal insufficiency is important to avoid the risks of over and under treatment including iatrogenic Cushing's syndrome and adrenal crisis. Objective: To establish a population pharmacokinetic model of hydrocortisone in children and use this to refine hydrocortisone replacement regimens. Design and methods: Pharmacokinetic study of hydrocortisone granules, available in 0.5, 1, 2 and 5 mg dose strengths, in 24 children with adrenal insufficiency aged 2 weeks to 6 years. Cortisol concentrations quantified by LC-MS/MS were used to refine an adult pharmacokinetic model to a paediatric population model which was then used to simulate seven different hydrocortisone treatment regimens. Results: Pre-dose cortisol levels were undetectable in 54% of the 24 children. The developed pharmacokinetic model had good predictive performance. Simulations for the seven treatment regimens using either three- or four-times daily dosing showed treatment regimens delivered an AUC(0-24h) within the 90% reference range for healthy children except in neonates where two regimens had an AUC below the 5th percentile. Cortisol concentrations at individual time points in the 24 h were outside the 90% reference range for healthy individuals in 50%, 55-65% and 70-75% for children, infants and neonates, respectively, with low cortisol levels being most prevalent. Conclusions: Current paediatric hydrocortisone treatment regimens based on either three- or four-times daily administration replicate cortisol exposure based on AUC(0-24h), but the majority of cortisol levels are above or below physiological cortisol levels with low levels very common before the next dose.

Objectives: Patients with congenital adrenal hyperplasia (CAH) require lifelong replacement therapy with glucocorticoids. Optimizing hydrocortisone therapy is challenging, since there are no established cortisol concentration targets other than the cortisol circadian rhythm profile. 17-hydroxyprogesterone (17-OHP) concentrations are elevated in these patients and commonly used to monitor therapy. This study aimed to characterize the pharmacokinetics/pharmacodynamics (PK/PD) of cortisol using 17-OHP as a biomarker in pediatric patients with CAH and to assess different hydrocortisone dosing regimens. Methods: Cortisol and 17-OHP concentrations from 30 CAH patients (7-17 years of age) receiving standard hydrocortisone replacement therapy (5-20 mg) twice (n = 17) or 3 times (n = 13) daily were used to develop a PK/PD model. Sequentially, simulated cortisol concentrations for clinically relevant 3- and 4-times daily dosing regimens were compared with cortisol and 17-OHP target ranges and to concentrations in healthy children. Results: Cortisol concentration-time profiles were accurately described by a 2-compartment model with first-order absorption and expected high bioavailability (82.6%). A time-delayed model with cortisol-mediated inhibition of 17-OHP synthesis accurately described 17-OHP concentrations. The cortisol concentration inhibiting 50% of 17-OHP synthesis was 48.6 nmol/L. A 4-times-daily dosing better attained the target ranges and mimicked the cortisol concentrations throughout the 24-hour period than 3-times-daily. Conclusions: A PK/PD model following hydrocortisone administration has been established. An improved dosing regimen of 38% at 06:00, 22% at 12:00, 17% at 18:00, and 22% at 24:00 of the daily hydrocortisone dose was suggested. The 4-times-daily dosing regimen was superior, avoiding subtherapeutic cortisol concentrations and better resembling the circadian rhythm of cortisol.

Introduction. Acute intermittent porphyria (AIP) is characterized by hepatic over-production of the heme precursors when aminolevulinic acid (ALA)-synthase 1 is induced by endogenous or environmental factors. The aim of this study was to develop a semi-mechanistic computational model to characterize urine accumulation of heme precursors during acute attacks based on experimental pharmacodynamics data and support the development of new therapeutic strategies. Methods: Male AIP mice received recurrent phenobarbital challenge starting on days 1, 9, 16 and 30. 24-h urine excretion of ALA, porphobilinogen (PBG) and porphyrins from challenges Dl, D9 and D30 constituted the training data set to build the mechanistic model using the population approach. In a second study, porphyrin and porphyrin precursor excretion from challenge D16 were used as a validation data set. Results: The computational model presented the following features: (i) urinary excretion of ALA, PBG and porphyrins was governed by unmeasured circulating heme precursor amounts, (ii) the circulating amounts of ALA and PBG were the precursors of circulating amounts of PBG and porphyrins, respectively, and (iii) the phenobarbital effect linearly increased the synthesis of circulating ALA and PBG levels. The model displayed good parameter precision (coefficient of variation below 32% in all parameters), and adequately described the experimental data. Finally, a theoretical hemin effect was implemented to illustrate the applicability of the model to dosage optimization in drug therapies. Conclusions: A semi-mechanistic disease model was successfully developed to describe the temporal evolution of urinary heme precursor excretion during recurrent biochemical-induced acute attacks in AIP mice. This model represents the first computational approach to explore and optimize current and new therapies.

The liver is a well-known immunotolerogenic environment, which provides the adequate setting for liver infectious pathogens persistence such as the hepatitis B virus (HBV). Consequently, HBV infection can derive in the development of chronic disease in a proportion of the patients. If this situation persists in time, chronic hepatitis B (CHB) would end in cirrhosis, hepatocellular carcinoma and eventually, the death of the patient. It is thought that this immunotolerogenic environment is the result of complex interactions between different elements of the immune system and the viral biology. Therefore, the purpose of this work is to unravel the mechanisms implied in the development of CHB and to design a tool able to help in the study of adequate therapies. Firstly, a conceptual framework with the main components of the immune system and viral dynamics was constructed providing an overall insight on the pathways and interactions implied in this disease. Secondly, a review of the literature was performed in a modular fashion: (i) viral dynamics, (ii) innate immune response, (iii) humoral and (iv) cellular adaptive immune responses and (v) tolerogenic aspects. Finally, the information collected was integrated into a single topological representation that could serve as the plan for the systems pharmacology model architecture. This representation can be considered as the previous unavoidable step to the construction of a quantitative model that could assist in biomarker and target identification, drug design and development, dosing optimization and disease progression analysis.

Context Optimization of hydrocortisone replacement therapy is important to prevent under- and over dosing. Hydrocortisone pharmacokinetics is complex as circulating cortisol is protein bound mainly to corticosteroid-binding globulin (CBG) that has a circadian rhythm. Objective A detailed analysis of the CBG circadian rhythm and its impact on cortisol exposure after hydrocortisone administration. Design and Methods CBG was measured over 24 hours in 14 healthy individuals and, employing a modelling and simulation approach using a semi-mechanistic hydrocortisone pharmacokinetic model, we evaluated the impact on cortisol exposure (area under concentration-time curve and maximum concentration of total cortisol) of hydrocortisone administration at different clock times and of the changing CBG concentrations. Results The circadian rhythm of CBG was well described with two cosine terms added to the baseline of CBG: baseline CBG was 21.8 mu g/mL and interindividual variability 11.9%; the amplitude for the 24 and 12 hours cosine functions were relatively small (24 hours: 5.53%, 12 hours: 2.87%) and highest and lowest CBG were measured at 18:00 and 02:00, respectively. In simulations, the lowest cortisol exposure was observed after administration of hydrocortisone at 23:00-02:00, whereas the highest was observed at 15:00-18:00. The differences between the highest and lowest exposure were minor (<= 12.2%), also regarding the free cortisol concentration and free fraction (<= 11.7%). Conclusions Corticosteroid-binding globulin has a circadian rhythm but the difference in cortisol exposure is <= 12.2% between times of highest and lowest CBG concentrations; therefore, hydrocortisone dose adjustment based on time of dosing to adjust for the CBG concentrations is unlikely to be of clinical benefit.

Xenograft mice are largely used to evaluate the efficacy of oncological drugs during preclinical phases of drug discovery and development. Mathematical models provide a useful tool to quantitatively characterize tumor growth dynamics and also optimize upcoming experiments. To the best of our knowledge, this is the first report where unperturbed growth of a large set of tumor cell lines (n=28) has been systematically analyzed using a previously proposed model of nonlinear mixed effects (NLME). Exponential growth was identified as the governing mechanism in the majority of the cell lines, with constant rate values ranging from 0.0204 to 0.203 day(-1). No common patterns could be observed across tumor types, highlighting the importance of combining information from different cell lines when evaluating drug activity. Overall, typical model parameters were precisely estimated using designs in which tumor size measurements were taken every 2 days. Moreover, reducing the number of measurements to twice per week, or even once per week for cell lines with low growth rates, showed little impact on parameter precision. However, a sample size of at least 50 mice is needed to accurately characterize parameter variability (i.e., relative S.E. values below 50%). This work illustrates the feasibility of systematically applying NLME models to characterize tumor growth in drug discovery and development, and constitutes a valuable source of data to optimize experimental designs by providing an a priori sampling window and minimizing the number of samples required.

Pharmacokinetic modeling, traditionally using drug exposure, is widely used to support decision-making in translational medicine and patient care. The development of mechanistic computational models that integrate drug concentrations at the site of action making use of existing knowledge opens a new paradigm in optimal dosing. (C) 2018 AACR.

Immunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k(2)) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies.

The aims of this work were as follows: 1) to develop a semi-mechanistic pharmacodynamic model describing tumor shrinkage after administration of a previously developed antitumor vaccine (CyaA-E7) in combination with CpG (a TLR9 ligand) and/or cyclophosphamide (CTX), and 2) to assess the translational capability of the model to describe tumor effects of different immune-based treatments. Population approach with NONMEM version 7.2 was used to analyze the previously published data. These data were generated by injecting 5 x 10(5) tumor cells expressing human papillomavirus (HPV)-E7 proteins into C57BL/6 mice. Large and established tumors were treated with CpG and/or CTX administered alone or in combination with CyaA-E7. Applications of the model were assessed by comparing model-based simulations with preclinical and clinical outcomes obtained from literature. CpG effects were modeled: 1) as an amplification of the immune signal triggered by the vaccine and 2) by shortening the delayed response of the vaccine. CTX effects were included through a direct decrease of the tumor-induced inhibition of vaccine efficacy over time, along with a delayed induction of tumor cell death. A pharmacodynamic model, built based on plausible biologic mechanisms known for the coadjuvants, successfully characterized tumor response in all experimental scenarios. The model developed was satisfactory applied to reproduce clinical outcomes when CpG or CTX was used in combination with different vaccines. The results found after simulation exercise indicated that the contribution of the coadjuvants to the tumor response elicited by vaccines can be predicted for other immune-based treatments.

nterleukin-12 (IL12) is a cytokine with potential applications in the treatment of cancer given the potent immune response that it triggers, in part due to its ability to stimulate expression of interferon-gamma (IFN gamma). To avoid the toxicity associated with systemic exposure to IL12, a high-capacity adenoviral vector carrying a liver-specific, mifepristone-inducible IL12 expression system (HC-Ad/RUmIL12) has been developed. However, the maintenance of IL12 expression at therapeutic levels is compromised by the inhibitory effect of IFN gamma on inducible systems. The aim of this work is to develop a semi-mechanistic model to characterize the relationship between IL12 and IFN gamma in wild-type and knock-out mice for the IFN gamma receptor treated with HC-Ad/RUmIL12 under different dosing regimens in order to better understand the key mechanisms controlling the system. Rapid binding was considered to account for target-mediated disposition exhibited by both cytokines (equilibrium dissociation constant were 18 and 2.28 pM for IL12 and IFN gamma, respectively). The final model included: (1) IFN gamma receptor turnover, (2) irreversible free cytokine elimination from the serum compartment, (3) internalization of the IL12 receptor complex, (4) IL12 expression upregulated by the co-administration of the adenoviral vector and mifepristone and downregulated by the IFN gamma receptor, and (5) synthesis of IFN gamma controlled by the relative increments in the bound IL12. In conclusion, a model simultaneously describing the kinetics of IL12 and IFN gamma in the context of gene therapy was developed and validated with additional data. The model was applied to design an experimental dosing protocol intended to maintain sustained therapeutic IL12 levels.

Interferon alpha linked to apolipoprotein A-I has been recently proposed as an improved interferon-based therapy. In the present study, we aimed to develop a computational model to gain further insight into the in vivo behaviour of this new fusion protein. In order to facilitate in vivo evaluation of interferon and the fusion protein without altering their biological properties, green fluorescent protein was incorporated into their structures. Kinetic and dynamic behaviour of both compounds was successfully described after plasmid hydrodynamic administration and in situ synthesis of the studied proteins. Results from the modelling exercise showed that apolipoprotein A-I conferred a modified kinetic behaviour, varying molecule distribution and prolonging half-life without altering liver dynamic performance. However, differences in the gene expression activity were observed at brain level between both compounds. Those differences could be explained by modifications in the dynamic, but also in the biodistribution properties, which would be worth evaluating in future experiments. Therefore, the modelling approach provided a global comprehension of a complex system and allowed us to compare the in vivo behaviour of both compounds and to identify critical aspects that might be important to understand the system better and suggests a need for new model-based experiments.