Mechanism-Based Modeling of Perioperative Variations in Hemoglobin Concentration in Patients Undergoing Laparoscopic Surgery.

BACKGROUND: Hemoglobin concentration ([Hb]) in the perioperative setting should be interpreted in the context of the variables and processes that may affect it to differentiate the dilution effects caused by changes in intravascular volume. However, it is unclear what variables and processes affect [Hb]. Here, we modeled the perioperative variations in [Hb] to identify the variables and processes that govern [Hb] and to describe their effects. METHODS: We first constructed a mechanistic framework based on the main variables and processes related to the perioperative [Hb] variations. We then prospectively studied patients undergoing laparoscopic surgery, divided into 2 consecutive cohorts for the development and validation of the model. The study protocol consisted of serial measurements of [Hb] along with recordings of hemoglobin mass loss, blood volume loss, fluid infusion, urine volume, and inflammatory biomarkers measurements, up to 96 hours postoperatively. Mathematical fitting was performed using nonlinear mixed-effects. Additionally, we performed simulations to explore the effects of blood loss and fluid therapy protocols on [Hb]. RESULTS: We studied 154 patients: 118 enrolled in the development group and 36 in the validation group. We characterized the perioperative course of [Hb] using a mass balance model that accounted for hemoglobin losses during surgery, and a 2-compartment model that estimated fluid kinetics and intravascular volume changes. During model development, we found that urinary fluid elimination represented only 24% of the total fluid elimination, and that total fluid elimination was inhibited after surgery in a time-dependent manner and influenced by age. Also, covariate evaluation showed a significant association between the type of surgery and proportion of fluid eliminated via urine. In contrast, neither the type of infused solution, blood volume loss nor inflammatory biomarkers were found to correlate with model parameters. In the validation analysis, the model demonstrated a considerable predictive capacity, with 95% of the predicted [Hb] within -4.4 and +5.5 g/L. Simulations demonstrated that hemoglobin mass loss determined most of the postoperative changes in [Hb], while intravascular volume changes due to fluid infusion, distribution, and elimination induced smaller but clinically relevant variations. Simulated patients receiving standard fluid therapy protocols exhibited a hemodilution effect that resulted in a [Hb] decrease between 7 and 15 g/L at the end of surgery, and which was responsible for the lowest [Hb] value during the perioperative period. CONCLUSIONS: Our model provides a mechanistic and quantitative understanding of the causes underlying the perioperative [Hb] variations.

Mechanistic characterization of oscillatory patterns in unperturbed tumor growth dynamics: The interplay between cancer cells and components of tumor microenvironment.

Mathematical modeling of unperturbed and perturbed tumor growth dynamics (TGD) in preclinical experiments provides an opportunity to establish translational frameworks. The most commonly used unperturbed tumor growth models (i.e. linear, exponential, Gompertz and Simeoni) describe a monotonic increase and although they capture the mean trend of the data reasonably well, systematic model misspecifications can be identified. This represents an opportunity to investigate possible underlying mechanisms controlling tumor growth dynamics through a mathematical framework. The overall goal of this work is to develop a data-driven semi-mechanistic model describing non-monotonic tumor growth in untreated mice. For this purpose, longitudinal tumor volume profiles from different tumor types and cell lines were pooled together and analyzed using the population approach. After characterizing the oscillatory patterns (oscillator half-periods between 8-11 days) and confirming that they were systematically observed across the different preclinical experiments available (p<10-9), a tumor growth model was built including the interplay between resources (i.e. oxygen or nutrients), angiogenesis and cancer cells. The new structure, in addition to improving the model diagnostic compared to the previously used tumor growth models (i.e. AIC reduction of 71.48 and absence of autocorrelation in the residuals (p>0.05)), allows the evaluation of the different oncologic treatments in a mechanistic way. Drug effects can potentially, be included in relevant processes taking place during tumor growth. In brief, the new model, in addition to describing non-monotonic tumor growth and the interaction between biological factors of the tumor microenvironment, can be used to explore different drug scenarios in monotherapy or combination during preclinical drug development.

A physiologically based pharmacokinetic model for V937 oncolytic virus in mice.

Introduction: Oncolytic viruses (OVs) represent a novel therapeutic strategy in oncology due to their capability to selectively infect and replicate in cancer cells, triggering a direct and/or immune-induced tumor lysis. However, the mechanisms governing OV pharmacokinetics are still poorly understood. This work aims to develop a physiologically based pharmacokinetic model of the novel OV, V937, in non-tumor-bearing mice to get a quantitative understanding of its elimination and tissue uptake processes. Materials and methods: Model development was performed using data obtained from 60 mice. Viral levels were quantified from eight tissues after a single intravenous V937 dose. An external dataset was used for model validation. This test set included multiple-dose experiments with different routes of administration. V937 distribution in each organ was described using a physiological structure based on mouse-specific organ blood flows and volumes. Analyses were performed using the non-linear mixed-effects approach with NONMEM 7.4. Results: Viral levels showed a drop from 108 to 105 copies/µg RNA at day 1 in blood, reflected in a high estimate of total clearance (18.2 mL/h). A well-stirred model provided an adequate description for all organs except the muscle and heart, where a saturable uptake process improved data description. The highest numbers of viral copies were observed in the brain, lymph node, kidney, liver, lung, and spleen on the first day after injection. On the other hand, the maximum amount of viral copies in the heart, muscle, and pancreas occurred 3 days after administration. Conclusion: To the best of our knowledge, this is the first physiologically based pharmacokinetic model developed to characterize OV biodistribution, representing a relevant source of quantitative knowledge regarding the in vivo behavior of OVs. This model can be further expanded by adding a tumor compartment, where OVs could replicate.

Assessment of Clinical Response to V937 Oncolytic Virus After Intravenous or Intratumoral Administration Using Physiologically-Based Modeling.

Oncolytic viruses (OVs) represent a potential therapeutic strategy in cancer treatment. However, there is currently a lack of comprehensive quantitative models characterizing clinical OV kinetics and distribution to the tumor. In this work, we present a mechanistic modeling framework for V937 OV, after intratumoral (i.t.) or intravascular (i.v.) administration in patients with cancer. A minimal physiologically-based pharmacokinetic model was built to characterize biodistribution of OVs in humans. Viral dynamics was incorporated at the i.t. cellular level and linked to tumor response, enabling the characterization of a direct OV killing triggered by the death of infected tumor cells and an indirect killing induced by the immune response. The model provided an adequate description of changes in V937 mRNA levels and tumor size obtained from phase I/II clinical trials after V937 administration. The model showed prominent role of viral clearance from systemic circulation and infectivity in addition to known tumor aggressiveness on clinical response. After i.v. administration, i.t. exposure of V937 was predicted to be several orders of magnitude lower compared with i.t. administration. These differences could be overcome if there is high virus infectivity and/or replication. Unfortunately, the latter process could not be identified at the current clinical setting. This work provides insights on selecting optimal OV considering replication rate and infectivity.

Prognostic Value of Serum Paraprotein Response Kinetics in Patients With Newly Diagnosed Multiple Myeloma.

INTRODUCTION: Response kinetics is a well-established prognostic marker in acute lymphoblastic leukemia. The situation is not clear in multiple myeloma (MM) despite having a biomarker for response monitoring (monoclonal component [MC]). MATERIALS AND METHODS: We developed a mathematical model to assess the prognostic value of serum MC response kinetics during 6 induction cycles, in 373 NDMM transplanted patients treated in the GEM2012Menos65 clinical trial. The model calculated a "resistance" parameter that reflects the stagnation in the response after an initial descent. RESULTS: Two patient subgroups were defined based on low and high resistance, that respectively captured sensitive and refractory kinetics, with progression-free survival (PFS) at 5 years of 72% and 59% (HR 0.64, 95% CI 0.44-0.93; P = .02). Resistance significantly correlated with depth of response measured after consolidation (80.9% CR and 68.4% minimal residual disease negativity in patients with sensitive vs. 31% and 20% in those with refractory kinetics). Furthermore, it modulated the impact of reaching CR after consolidation; thus, within CR patients those with refractory kinetics had significantly shorter PFS than those with sensitive kinetics (median 54 months vs. NR; P = .02). Minimal residual disease negativity abrogated this effect. Our study also questions the benefit of rapid responders compared to late responders (5-year PFS 59.7% vs. 76.5%, respectively [P < .002]). Of note, 85% of patients considered as late responders were classified as having sensitive kinetics. CONCLUSION: This semi-mechanistic modeling of M-component kinetics could be of great value to identify patients at risk of early treatment failure, who may benefit from early rescue intervention strategies.

Semimechanistic models to relate noxious stimulation, movement, and pupillary dilation responses in the presence of opioids.

Intraoperative targeting of the analgesic effect still lacks an optimal solution. Opioids are currently the main drug used to achieve antinociception, and although underdosing can lead to an increased stress response, overdose can also lead to undesirable adverse effects. To better understand how to achieve the optimal analgesic effect of opioids, we studied the influence of remifentanil on the pupillary reflex dilation (PRD) and its relationship with the reflex movement response to a standardized noxious stimulus. The main objective was to generate population pharmacodynamic models relating remifentanil predicted concentrations to movement and to pupillary dilation during general anesthesia. A total of 78 patients undergoing gynecological surgery under general anesthesia were recruited for the study. PRD and movement response to a tetanic stimulus were measured multiple times before and after surgery. We used nonlinear mixed effects modeling to generate a population pharmacodynamic model to describe both the time profiles of PRD and movement responses to noxious stimulation. Our model demonstrated that movement and PRD are equally depressed by remifentanil. Using the developed model, we changed the intensity of stimulation and simulated remifentanil predicted concentrations maximizing the probability of absence of movement response. An estimated effect site concentration of 2 ng/ml of remifentanil was found to inhibit movement to a tetanic stimulation with a probability of 81%.

Relevance of primary lesion location, tumour heterogeneity and genetic mutation demonstrated through tumour growth inhibition and overall survival modelling in metastatic colorectal cancer.

AIMS: The aims of this work were to build a semi-mechanistic tumour growth inhibition (TGI) model for metastatic colorectal cancer (mCRC) patients receiving either cetuximab + chemotherapy or chemotherapy alone and to identify early predictors of overall survival (OS). METHODS: A total of 1716 patients from 4 mCRC clinical studies were included in the analysis. The TGI model was built with 8973 tumour size measurements where the probability of drop-out was also included and modelled as a time-to-event variable using parametric survival models, as it was the case in the OS analysis. The effects of patient- and tumour-related covariates on model parameters were explored. RESULTS: Chemotherapy and cetuximab effects were included in an additive form in the TGI model. Development of resistance was found to be faster for chemotherapy (drug effect halved at wk 8) compared to cetuximab (drug effect halved at wk 12). KRAS wild-type status and presenting a right-sided primary lesion were related to a 3.5-fold increase in cetuximab drug effect and a 4.7× larger cetuximab resistance, respectively. The early appearance of a new lesion (HR = 4.14), a large tumour size at baseline (HR = 1.62) and tumour heterogeneity (HR = 1.36) were the main predictors of OS. CONCLUSIONS: Semi-mechanistic TGI and OS models have been developed in a large population of mCRC patients receiving chemotherapy in combination or not with cetuximab. Tumour-related predictors, including a machine learning derived-index of tumour heterogeneity, were linked to changes in drug effect, resistance to treatment or OS, contributing to the understanding of the variability in clinical response.

Semi-Mechanistic Model for the Antitumor Response of a Combination Cocktail of Immuno-Modulators in Non-Inflamed (Cold) Tumors.

Immune checkpoint inhibitors, administered as single agents, have demonstrated clinical efficacy. However, when treating cold tumors, different combination strategies are needed. This work aims to develop a semi-mechanistic model describing the antitumor efficacy of immunotherapy combinations in cold tumors. Tumor size of mice treated with TC-1/A9 non-inflamed tumors and the drug effects of an antigen, a toll-like receptor-3 agonist (PIC), and an immune checkpoint inhibitor (anti-programmed cell death 1 antibody) were modeled using Monolix and following a middle-out strategy. Tumor growth was best characterized by an exponential model with an estimated initial tumor size of 19.5 mm3 and a doubling time of 3.6 days. In the treatment groups, contrary to the lack of response observed in monotherapy, combinations including the antigen were able to induce an antitumor response. The final model successfully captured the 23% increase in the probability of cure from bi-therapy to triple-therapy. Moreover, our work supports that CD8+ T lymphocytes and resistance mechanisms are strongly related to the clinical outcome. The activation of antigen-presenting cells might be needed to achieve an antitumor response in reduced immunogenic tumors when combined with other immunotherapies. These models can be used as a platform to evaluate different immuno-oncology combinations in preclinical and clinical scenarios.

A quantitative systems pharmacology model for acute viral hepatitis B.

Hepatitis B liver infection is caused by hepatitis B virus (HBV) and represents a major global disease problem when it becomes chronic, as is the case for 80-90% of vertical or early life infections. However, in the vast majority (>95%) of adult exposures, the infected individuals are capable of mounting an effective immune response leading to infection resolution. A good understanding of HBV dynamics and the interaction between the virus and immune system during acute infection represents an essential step to characterize and understand the key biological processes involved in disease resolution, which may help to identify potential interventions to prevent chronic hepatitis B. In this work, a quantitative systems pharmacology model for acute hepatitis B characterizing viral dynamics and the main components of the innate, adaptive, and tolerant immune response has been successfully developed. To do so, information from multiple sources and across different organization levels has been integrated in a common mechanistic framework. The final model adequately describes the chronology and plausibility of an HBV-triggered immune response, as well as clinical data from acute patients reported in the literature. Given the holistic nature of the framework, the model can be used to illustrate the relevance of the different immune pathways and biological processes to ultimate response, observing the negligible contribution of the innate response and the key contribution of the cellular response on viral clearance. More specifically, moderate reductions of the proliferation of activated cytotoxic CD8+ lymphocytes or increased immunoregulatory effects can drive the system towards chronicity.

Mechanistic Modeling of a Novel Oncolytic Virus, V937, to Describe Viral Kinetic and Dynamic Processes Following Intratumoral and Intravenous Administration.

V937 is an investigational novel oncolytic non-genetically modified Kuykendall strain of Coxsackievirus A21 which is in clinical development for the treatment of advanced solid tumor malignancies. V937 infects and lyses tumor cells expressing the intercellular adhesion molecule I (ICAM-I) receptor. We integrated in vitro and in vivo data from six different preclinical studies to build a mechanistic model that allowed a quantitative analysis of the biological processes of V937 viral kinetics and dynamics, viral distribution to tumor, and anti-tumor response elicited by V937 in human xenograft models in immunodeficient mice following intratumoral and intravenous administration. Estimates of viral infection and replication which were calculated from in vitro experiments were successfully used to describe the tumor response in vivo under various experimental conditions. Despite the predicted high clearance rate of V937 in systemic circulation (t1/2 = 4.3 min), high viral replication was observed in immunodeficient mice which resulted in tumor shrinkage with both intratumoral and intravenous administration. The described framework represents a step towards the quantitative characterization of viral distribution, replication, and oncolytic effect of a novel oncolytic virus following intratumoral and intravenous administrations in the absence of an immune response. This model may further be expanded to integrate the role of the immune system on viral and tumor dynamics to support the clinical development of oncolytic viruses.

The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives.

Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.

Dual activity of PD-L1 targeted Doxorubicin immunoliposomes promoted an enhanced efficacy of the antitumor immune response in melanoma murine model.

BACKGROUND: The immunomodulation of the antitumor response driven by immunocheckpoint inhibitors (ICIs) such as PD-L1 (Programmed Death Ligand-1) monoclonal antibody (α-PD-L1) have shown relevant clinical outcomes in a subset of patients. This fact has led to the search for rational combinations with other therapeutic agents such as Doxorubicin (Dox), which cytotoxicity involves an immune activation that may enhance ICI response. Therefore, this study aims to evaluate the combination of chemotherapy and ICI by developing Dox Immunoliposomes functionalized with monovalent-variable fragments (Fab') of α-PD-L1. RESULTS: Immunoliposomes were assayed in vitro and in vivo in a B16 OVA melanoma murine cell line over-expressing PD-L1. Here, immune system activation in tumor, spleen and lymph nodes, together with the antitumor efficacy were evaluated. Results showed that immunoliposomes bound specifically to PD-L1+ cells, yielding higher cell interaction and Dox internalization, and decreasing up to 30-fold the IC50, compared to conventional liposomes. This mechanism supported a higher in vivo response. Indeed, immunoliposomes promoted full tumor regression in 20% of mice and increased in 1 month the survival rate. This formulation was the only treatment able to induce significant (p < 0.01) increase of activated tumor specific cytotoxic T lymphocytes at the tumor site. CONCLUSION: PD-L1 targeted liposomes encapsulating Dox have proved to be a rational combination able to enhance the modulation of the immune system by blocking PD-L1 and selectively internalizing Dox, thus successfully providing a dual activity offered by both, chemo and immune therapeutic strategies.

Modulation of intratumoural myeloid cells, the hallmark of the anti-tumour efficacy induced by a triple combination: tumour-associated peptide, TLR-3 ligand and α-PD-1.

BACKGROUND: Anti-programmed cell death 1 (PD-1)/programmed death-ligand 1 (PD-L1) monoclonal antibodies (mAbs) show remarkable clinical anti-tumour efficacy. However, rational combinations are needed to extend the clinical benefit to primary resistant tumours. The design of such combinations requires the identification of the kinetics of critical immune cell populations in the tumour microenvironment. METHODS: In this study, we compared the kinetics of immune cells in the tumour microenvironment upon treatment with immunotherapy combinations with different anti-tumour efficacies in the non-inflamed tumour model TC-1/A9. Tumour-bearing C57BL/6J mice were treated with all possible combinations of a human papillomavirus (HPV) E7 long peptide, polyinosinic-polycytidylic acid (PIC) and anti-PD-1 mAb. Tumour growth and kinetics of the relevant immune cell populations were assessed over time. The involvement of key immune cells was confirmed by depletion with mAbs and immunophenotyping with multiparametric flow cytometry. RESULTS: The maximum anti-tumour efficacy was achieved after intratumoural administration of HPV E7 long peptide and PIC combined with the systemic administration of anti-PD-1 mAb. The intratumoural immune cell kinetics of this combination was characterised by a biphasic immune response. An initial upsurge of proinflammatory myeloid cells led to a further rise in effector CD8+ T lymphocytes at day 8. Depletion of either myeloid cells or CD8+ T lymphocytes diminished the anti-tumour efficacy of the combination. CONCLUSIONS: The anti-tumour efficacy of a successful immunotherapy combination in a non-inflamed tumour model relies on an early inflammatory process that remodels the myeloid cell compartment.

Beyond Deterministic Models in Drug Discovery and Development.

The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug-disease models more versatile and realistic.

Pharmacokinetic Profiles Determine Optimal Combination Treatment Schedules in Computational Models of Drug Resistance.

Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available in vitro data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non-small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. SIGNIFICANCE: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.

Predicting circulating biomarker response and its impact on the survival of advanced melanoma patients treated with adjuvant therapy.

Advanced melanoma remains a disease with poor prognosis. Several serologic markers have been investigated to help monitoring and prognostication, but to date only lactate dehydrogenase (LDH) has been validated as a standard prognostic factor biomarker for this disease by the American Joint Committee on Cancer. In this work, we built a semi-mechanistic model to explore the relationship between the time course of several circulating biomarkers and overall or progression free survival in advanced melanoma patients treated with adjuvant high-dose interferon-[Formula: see text]. Additionally, due to the adverse interferon tolerability, a semi-mechanistic model describing the side effects of the treatment in the absolute neutrophil counts is proposed in order to simultaneously analyze the benefits and toxic effects of this treatment. The results of our analysis suggest that the relative change from baseline of LDH was the most significant predictor of the overall survival of the patients. Unfortunately, there was no significant difference in the proportion of patients with elevated serum biomarkers between the patients who recurred and those who remained free of disease. Still, we believe that the modelling framework presented in this work of circulating biomarkers and adverse effects could constitute an additional strategy for disease monitoring in advance melanoma patients.

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

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.

Modelling of neutrophil dynamics in children receiving busulfan or treosulfan for haematopoietic stem cell transplant conditioning.

AIMS: Busulfan and treosulfan are cytotoxic agents used in the conditioning regime prior to paediatric haematopoietic stem cell transplantation (HSCT). These agents cause suppression of myeloid cells leaving patients severely immunocompromised in the early post-HSCT period. The main objectives were: (i) to establish a mechanistic pharmacokinetic-pharmacodynamic (PKPD) model for the treatment and engraftment effects on neutrophil counts comparing busulfan and treosulfan-based conditioning, and (ii) to explore current dosing schedules with respect to time to HSCT. METHODS: Data on 126 patients, 72 receiving busulfan (7 months-18 years, 5.1-47.0 kg) and 54 treosulfan (4 months-17 years, 3.8-35.8 kg), were collected. In total, 8935 neutrophil count observations were recorded during the study period in addition to drug concentrations to develop a mechanistic PKPD model. Absolute neutrophil count profiles were modelled semimechanistically, accounting for transplant effects and differing set points pre- and post-transplant. RESULTS: PK were best described by 2-compartment models for both drugs. The Friberg semimechanistic neutropenia model was applied with a linear model for busulfan and a maximum efficacy model for treosulfan describing drug effects at various stages of neutrophil maturation. System parameters were consistent across both drugs. The HSCT was represented by an amount of progenitor cells enhancing the neutrophils' proliferation and maturation compartments. Alemtuzumab was found to enhance the proliferative rate under which the absolute neutrophil count begin to grow after HSCT. CONCLUSION: A semimechanistic PKPD model linking exposure to either busulfan or treosulfan to the neutrophil reconstitution dynamics was successfully built. Alemtuzumab coadministration enhanced the neutrophil proliferative rate after HSCT. Treosulfan administration was suggested to be delayed with respect to time to HSCT, leaving less time between the end of the administration and stem cell infusion.

Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2',2'-difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer.

The aim of this work is to build a mechanistic multiscale pharmacokinetic model for the anticancer drug 2',2'-difluorodeoxycytidine (gemcitabine, dFdC), able to describe the concentrations of dFdC metabolites in the pancreatic tumor tissue in dependence of physiological and genetic patient characteristics, and, more in general, to explore the capabilities and limitations of this kind of modeling strategy. A mechanistic model characterizing dFdC metabolic pathway (metabolic network) was developed using in vitro literature data from two pancreatic cancer cell lines. The network was able to describe the time course of extracellular and intracellular dFdC metabolites concentrations. Moreover, a physiologically-based pharmacokinetic model was developed to describe clinical dFdC profiles by using enzymatic and physiological information available in the literature. This model was then coupled with the metabolic network to describe the dFdC active metabolite profile in the pancreatic tumor tissue. Finally, global sensitivity analysis was performed to identify the parameters that mainly drive the interindividual variability for the area under the curve (AUC) of dFdC in plasma and of its active metabolite (dFdCTP) in tumor tissue. From this analysis, cytidine deaminase (CDA) concentration was identified as the main driver of plasma dFdC AUC interindividual variability, whereas CDA and deoxycytidine kinase concentration mainly explained the tumor dFdCTP AUC variability. However, the lack of in vitro and in vivo information needed to characterize key model parameters hampers the development of this kind of mechanistic approach. Further studies to better characterize pancreatic cell lines and patient enzymes polymorphisms are encouraged to refine and validate the current model.