Saroja Ramanujan

The Type 1 Diabetes PhysioLab® Platform: a validated physiologically based mathematical model of pathogenesis in the non‐obese diabetic mouse

Type 1 diabetes is an autoimmune disease whose clinical onset signifies a lifelong requirement for insulin therapy and increased risk of medical complications. To increase the efficiency and confidence with which drug candidates advance to human type 1 diabetes clinical trials, we have generated and validated a mathematical model of type 1 diabetes pathophysiology in a well‐characterized animal model of spontaneous type 1 diabetes, the non‐obese diabetic (NOD) mouse. The model is based on an extensive survey of the public literature and input from an independent scientific advisory board. It reproduces key disease features including activation and expansion of autoreactive lymphocytes in the pancreatic lymph nodes (PLNs), islet infiltration and β cell loss leading to hyperglycaemia. The model uses ordinary differential and algebraic equations to represent the pancreas and PLN as well as dynamic interactions of multiple cell types (e.g. dendritic cells, macrophages, CD4+ T lymphocytes, CD8+ T lymphocytes, regulatory T cells, β cells). The simulated features of untreated pathogenesis and disease outcomes for multiple interventions compare favourably with published experimental data. Thus, a mathematical model reproducing type 1 diabetes pathophysiology in the NOD mouse, validated based on accurate reproduction of results from multiple published interventions, is available for in silico hypothesis testing. Predictive biosimulation research evaluating therapeutic strategies and underlying biological mechanisms is intended to deprioritize hypotheses that impact disease outcome weakly and focus experimental research on hypotheses likely to provide insight into the disease and its treatment.

Mechanism-Based Pharmacokinetic/Pharmacodynamic Model for THIOMAB™ Drug Conjugates

ABSTRACTPurposeTHIOMAB™ drug conjugates (TDCs) with engineered cysteine residues allow site-specific drug conjugation and defined Drug-to-Antibody Ratios (DAR). In order to help elucidate the impact of drug-loading, conjugation site, and subsequent deconjugation on pharmacokinetics and efficacy, we have developed an integrated mathematical model to mechanistically characterize pharmacokinetic behavior and preclinical efficacy of MMAE conjugated TDCs with different DARs. General applicability of the model structure was evaluated with two different TDCs.MethodPharmacokinetics studies were conducted for unconjugated antibody and purified TDCs with DAR-1, 2 and 4 for trastuzumab TDC and Anti-STEAP1 TDC in mice. Total antibody concentrations and individual DAR fractions were measured. Efficacy studies were performed in tumor-bearing mice.ResultsAn integrated model consisting of distinct DAR species (DAR0-4), each described by a two-compartment model was able to capture the experimental data well. Time series measurements of each Individual DAR species allowed for the incorporation of site-specific drug loss through deconjugation and the results suggest a higher deconjugation rate from heavy chain site HC-A114C than the light chain site LC-V205C. Total antibody concentrations showed multi-exponential decline, with a higher clearance associated with higher DAR species. The experimentally observed effects of TDC on tumor growth kinetics were successfully described by linking pharmacokinetic profiles to DAR-dependent killing of tumor cells.ConclusionResults from the integrated model evaluated with two different TDCs highlight the impact of DAR and site of conjugation on pharmacokinetics and efficacy. The model can be used to guide future drug optimization and in-vivo studies.

A Mechanistic Systems Pharmacology Model for Prediction of LDL Cholesterol Lowering by PCSK9 Antagonism in Human Dyslipidemic Populations

PCSK9 is a promising target for the treatment of hyperlipidemia and cardiovascular disease. A Quantitative Systems Pharmacology model of the mechanisms of action of statin and anti-PCSK9 therapies was developed to predict low density lipoprotein (LDL) changes in response to anti-PCSK9 mAb for different treatment protocols and patient subpopulations. Mechanistic interactions and cross-regulation of LDL, LDL receptor, and PCSK9 were modeled, and numerous virtual subjects were developed and validated against clinical data. Simulations predict a slightly greater maximum percent reduction in LDL cholesterol (LDLc) when anti-PCSK9 is administered on statin background therapy compared to as a monotherapy. The difference results primarily from higher PCSK9 levels in patients on statin background. However, higher PCSK9 levels are also predicted to increase clearance of anti-PCSK9, resulting in a faster rebound of LDLc. Simulations of subjects with impaired LDL receptor (LDLR) function predict compromised anti-PCSK9 responses in patients such as homozygous familial hypercholesterolemics, whose functional LDLR is below 10% of normal. CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e149; doi:10.1038/psp.2014.47; published online 26 November 2014.PCSK9 is a promising target for the treatment of hyperlipidemia and cardiovascular disease. A Quantitative Systems Pharmacology model of the mechanisms of action of statin and anti‐PCSK9 therapies was developed to predict low density lipoprotein (LDL) changes in response to anti‐PCSK9 mAb for different treatment protocols and patient subpopulations. Mechanistic interactions and cross‐regulation of LDL, LDL receptor, and PCSK9 were modeled, and numerous virtual subjects were developed and validated against clinical data. Simulations predict a slightly greater maximum percent reduction in LDL cholesterol (LDLc) when anti‐PCSK9 is administered on statin background therapy compared to as a monotherapy. The difference results primarily from higher PCSK9 levels in patients on statin background. However, higher PCSK9 levels are also predicted to increase clearance of anti‐PCSK9, resulting in a faster rebound of LDLc. Simulations of subjects with impaired LDL receptor (LDLR) function predict compromised anti‐PCSK9 responses in patients such as homozygous familial hypercholesterolemics, whose functional LDLR is below 10% of normal.

The virtual NOD mouse: applying predictive biosimulation to research in type 1 diabetes.

Abstract:  Type 1 diabetes is a complex, multifactorial disease characterized by T cell–mediated autoimmune destruction of insulin‐secreting pancreatic β cells. To facilitate research in type 1 diabetes, a large‐scale dynamic mathematical model of the female non‐obese diabetic (NOD) mouse was developed. In this model, termed the Entelos® Type 1 Diabetes PhysioLab® platform, virtual NOD mice are constructed by mathematically representing components of the immune system and islet β cell physiology important for the pathogenesis of type 1 diabetes. This report describes the scope of the platform and illustrates some of its capabilities. Specifically, using two virtual NOD mice with either average or early diabetes‐onset times, we demonstrate the reproducibility of experimentally observed dynamics involved in diabetes progression, therapeutic responses to exogenous IL‐10, and heterogeneity in disease onset. Additionally, we use the Type 1 Diabetes PhysioLab platform to investigate the impact of disease heterogeneity on the effectiveness of exogenous IL‐10 therapy to prevent diabetes onset. Results indicate that the inability of a previously published IL‐10 therapy protocol to protect NOD mice who exhibit early diabetes onset is due to high levels of pancreatic lymph node (PLN) inflammation, islet infiltration, and β cell destruction at the time of treatment initiation. Further, simulation indicates that earlier administration of the treatment protocol can prevent NOD mice from developing diabetes by initiating treatment during the period when the disease is still sensitive to IL‐10s protective function.

Evaluation of HDL-modulating interventions for cardiovascular risk reduction using a systems pharmacology approach.

The recent failures of cholesteryl ester transport protein inhibitor drugs to decrease CVD risk, despite raising HDL cholesterol (HDL-C) levels, suggest that pharmacologic increases in HDL-C may not always reflect elevations in reverse cholesterol transport (RCT), the process by which HDL is believed to exert its beneficial effects. HDL-modulating therapies can affect HDL properties beyond total HDL-C, including particle numbers, size, and composition, and may contribute differently to RCT and CVD risk. The lack of validated easily measurable pharmacodynamic markers to link drug effects to RCT, and ultimately to CVD risk, complicates target and compound selection and evaluation. In this work, we use a systems pharmacology model to contextualize the roles of different HDL targets in cholesterol metabolism and provide quantitative links between HDL-related measurements and the associated changes in RCT rate to support target and compound evaluation in drug development. By quantifying the amount of cholesterol removed from the periphery over the short-term, our simulations show the potential for infused HDL to treat acute CVD. For the primary prevention of CVD, our analysis suggests that the induction of ApoA-I synthesis may be a more viable approach, due to the long-term increase in RCT rate.

Mathematical PKPD and safety model of bispecific TfR/BACE1 antibodies for the optimization of antibody uptake in brain

Treatment of diseases of the central nervous system by monoclonal antibodies may be limited by the restricted uptake of antibodies across the blood-brain barrier (BBB). An antibody targeting transferrin receptor (TfR) has been shown to take advantage of the receptor-mediated transcytosis properties of TfR in order to cross the BBB in mice, with the uptake in the brain being dependent on the affinity to TfR. In the bispecific format with arms targeting both TfR and β-secretase 1 (BACE1), altering the affinity to TfR has been shown to impact systemic exposure and safety profiles. In this work, a mathematical model incorporating pharmacokinetic/pharmacodynamic (PKPD) and safety profiles is developed for bispecific TfR/BACE1 antibodies with a range of affinities to TfR in order to guide candidate selection. The model captures the dependence of both systemic and brain exposure on TfR affinity and the subsequent impact on brain Aβ40 lowering and circulating reticulocyte levels. Model simulations identify the optimal affinity for the TfR arm of the bispecific to maximize Aβ reduction while maintaining reticulocyte levels. The model serves as a useful tool to prioritize and optimize preclinical studies and has been used to support the selection of additional candidates for further development.

Challenges and opportunities for quantitative clinical pharmacology in cancer immunotherapy: Something old, something new, something borrowed and something blue

Cancer immunotherapy (CIT) initiates or enhances the host immune response against cancer. Following decades of development, patients with previously few therapeutic options may now benefit from CIT. Although the quantitative clinical pharmacology (qCP) of previous classes of anticancer drugs has matured during this time, application to CIT may not be straightforward since CIT acts via the immune system. Here we discuss where qCP approaches might best borrow or start anew for CIT.

Dosing and Timing Effects of Anti-CD40L Therapy

Abstract:  Several publications describing the use of anti‐CD40L monoclonal antibodies (anti‐CD40L) for the treatment of type 1 diabetes in non‐obese diabetic (NOD) mice have reported different treatment responses to similar protocols. The Entelos® Type 1 Diabetes PhysioLab® platform, a dynamic large‐scale mathematical model of the pathogenesis of type 1 diabetes, was used to study the effects of anti‐CD40L therapy in silico. An examination of the impact of pharmacokinetic variability and the heterogeneity of disease progression rate on therapeutic outcome provided insights that could reconcile the apparently conflicting data. Optimal treatment protocols were identified by exploring the dynamics of key pathophysiological pathways.

Quantitative systems pharmacology: a promising approach for translational pharmacology

Biopharmaceutical companies have increasingly been exploring Quantitative Systems Pharmacology (QSP) as a potential avenue to address current challenges in drug development. In this paper, we discuss the application of QSP modeling approaches to address challenges in the translational of preclinical findings to the clinic, a high risk area of drug development. Three cases have been highlighted with QSP models utilized to inform different questions in translational pharmacology. In the first, a mechanism based asthma model is used to evaluate efficacy and inform biomarker strategy for a novel bispecific antibody. In the second case study, a mitogen-activated protein kinase (MAPK) pathway signaling model is used to make translational predictions on clinical response and evaluate novel combination therapies. In the third case study, a physiologically based pharmacokinetic (PBPK) model it used to guide administration of oseltamivir in pediatric patients.

Clinical responses to ERK inhibition in BRAF V600E -mutant colorectal cancer predicted using a computational model

Approximately 10% of colorectal cancers harbor BRAFV600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes (“waterfall plot”). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.Systems pharmacology:Predicting efficacy of novel anti-cancer drugs in colorectal cancerWhile cancer drug development relies on experimental tumor models for testing, results observed in these systems often fail to translate clinically. Kirouac et al. demonstrate how computational systems modelling can help bridge this divide. Focusing on a class of colorectal cancers with poor prognosis (those with a mutant form of the BRAF oncogene) they develop a mathematical model linking drug exposure, via cellular signal transduction, to tumor growth. By triangulating experimental data from multiple cell lines and mouse models, with results from three clinical trials of related drugs, the model accurately predicted tumor shrinkage observed in a first-in-human study of GDC-0994, an ERK inhibitor. Simulations were then used to explore strategies for increasing the activity of this class of drugs (MAPK pathway inhibitors) via combinations, alternate dosing regimens, and predictive biomarkers to guide future clinical studies. Extended to other cancer types and drugs, the approach could streamline early clinical development.