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Age‐related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease‐modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

There is strong interest in developing predictive models to better understand individual heterogeneity and disease progression in Alzheimer's disease (AD). We have built upon previous longitudinal AD progression models, using a nonlinear, mixed‐effect modeling approach to predict Clinical Dementia Rating Scale – Sum of Boxes (CDR‐SB) progression. Data from the Alzheimer's Disease Neuroimaging Initiative (observational study) and placebo arms from four interventional trials (N = 1093) were used for model building. The placebo arms from two additional interventional trials (N = 805) were used for external model validation. In this modeling framework, CDR‐SB progression over the disease trajectory timescale was obtained for each participant by estimating disease onset time (DOT). Disease progression following DOT was described by both global progression rate (RATE) and individual progression rate (α). Baseline Mini‐Mental State Examination and CDR‐SB scores described the interindividual variabilities in DOT and α well. This model successfully predicted outcomes in the external validation datasets, supporting its suitability for prospective prediction and use in design of future trials. By predicting individual participants' disease progression trajectories using baseline characteristics and comparing these against the observed responses to new agents, the model can help assess treatment effects and support decision making for future trials.

Receptor‐interacting protein 1 (RIP1) is a key regulator of multiple signaling pathways that mediate inflammatory responses and cell death. RIP1 kinase activity mediates apoptosis and necroptosis induced by tumor necrosis factor (TNF)‐α, Toll‐like receptors, and ischemic tissue damage. RIP1 has been implicated in several human pathologies and consequently, RIP1 inhibition may represent a therapeutic approach for diseases dependent on RIP1‐mediated inflammation and cell death. GDC‐8264 is a potent, selective, and reversible small molecule inhibitor of RIP1 kinase activity. This phase I, randomized, placebo‐controlled, double‐blinded trial examined safety, pharmacokinetics (PKs), and pharmacodynamics (PDs) of single‐ (5–225 mg) and multiple‐ (50 and 100 mg once daily, up to 14 days) ascending oral doses of GDC‐8264 in healthy volunteers, and also tested the effect of food on the PKs of GDC‐8264. All adverse events in GDC‐8264‐treated subjects in both stages were mild. GDC‐8264 exhibited dose‐proportional increases in systemic exposure; the mean terminal half‐life ranged from 10–13 h, with limited accumulation on multiple dosing (accumulation ratio [AR] ~ 1.4); GDC‐8264 had minimal renal excretion at all doses. A high‐fat meal had no significant effect on the PKs of GDC‐8264. In an ex vivo stimulation assay of whole blood, GDC‐8264 rapidly and completely inhibited release of CCL4, a downstream marker of RIP1 pathway activation, indicating a potent pharmacological effect. Based on PK‐PD modeling, the GDC‐8264 half‐maximal inhibitory concentration for the inhibition of CCL4 release was estimated to be 0.58 ng/mL. The favorable safety, PKs, and PDs of GDC‐8264 support its further development for treatment of RIP1‐driven diseases.

HER2-positive breast cancer (BC) is a rapidly growing and aggressive BC subtype that predominantly affects younger women. Despite improvements in patient outcomes with anti-HER2 therapy, primary and/or acquired resistance remain a major clinical challenge. Here, we sought to use a quantitative systems pharmacological (QSP) approach to evaluate the efficacy of lapatinib (LAP), abemaciclib (ABE) and 5-fluorouracil (5-FU) mono- and combination therapies in JIMT-1 cells, a HER2+ BC cell line exhibiting intrinsic resistance to trastuzumab. Concentration–response relationships and temporal profiles of cellular viability were assessed upon exposure to single agents and their combinations. To quantify the nature and intensity of drug-drug interactions, pharmacodynamic cellular response models were generated, to characterize single agent and combination time course data. Temporal changes in cell-cycle phase distributions, intracellular protein signaling, and JIMT-1 cellular viability were quantified, and a systems-based protein signaling network model was developed, integrating protein dynamics to drive the observed changes in cell viability. Global sensitivity analyses for each treatment arm were performed, to identify the most influential parameters governing cellular responses. Our QSP model was able to adequately characterize protein dynamic and cellular viability trends following single and combination drug exposure. Moreover, the model and subsequent sensitivity analyses suggest that the activation of the stress pathway, through pJNK, has the greatest impact over the observed declines of JIMT-1 cell viability in vitro. These findings suggest that dual HER2 and CDK 4/6 inhibition may be a promising novel treatment strategy for refractory HER2+ BC, however, proof-of-concept in vivo studies are needed to further evaluate the combined use of these therapies.

Migoprotafib is a potent and selective inhibitor of Src homology‐2 domain‐containing phosphatase 2 (SHP2) under investigation in Phase I (PhI) trials both as monotherapy and in combination with multiple therapies for patients with metastatic solid tumors. The PhI study reported herein aimed to assess both a new tablet formulation and the impact of a high‐fat meal on the pharmacokinetics (PK) of migoprotafib at the recommended Phase II dose (RP2D) of 60 mg in patients. In two distinct cohorts, patients were administered migoprotafib as a tablet or capsule formulation to assess the impact of formulation, or as a tablet under fasted or fed conditions to assess the impact of food. In an evaluation of the effect of formulation in 20 subjects, the geometric mean ratios (GMRs) [90% CI] of the tablet: capsule formulation were 101 [89.8–114], and 103 [86.2–122], for AUC0‐∞ and Cmax, respectively. Consumption of a high‐fat meal prior to migoprotafib administration resulted in comparable AUC0‐∞ and reduced Cmax compared to fasted administration in 17 subjects, with GMRs [90% CI] of 92.2 [74.5–114], and 42.4 [30.6–58.9], for AUC0‐∞ and Cmax, respectively. These findings informed subsequent dosing recommendations for migoprotafib in ongoing and future clinical studies. Study Highlights What is the current knowledge on the topic? ○Published data on the pharmacokinetics (PK) of migoprotafib are limited to its first‐in‐human study, where it was administered as a single‐agent capsule under fasted conditions. What question did this study address? ○In this study, the effects of formulation and food on the PK of migoprotafib were evaluated using a clinically relevant dose of 60 mg in cancer patients. What does this study add to our knowledge? ○The comparable exposure observed between the capsule and tablet formulations enabled a transition to the more commercially viable tablet for all ongoing and subsequent clinical studies. Furthermore, the food effect results provide critical guidance for dosing recommendations using a combination specific approach; for combinations where efficacy is not primarily Cmax‐driven, migoprotafib may be administered without regard to food. How might this change clinical pharmacology or translational science? ○This work provides a framework for early evaluation of the individual effects of formulation and food, at a clinically relevant dose, by conducting the assessments within an ongoing Phase I oncology study.

Quantitative systems pharmacology (QSP) models are often implemented using a wide variety of technical workflows and methodologies. To facilitate reproducibility, transparency, portability, and reuse for QSP models, we have developed gQSPSim, a graphical user interface–based MATLAB application that performs key steps in QSP model development and analyses. The capabilities of gQSPSim include (i) model calibration using global and local optimization methods, (ii) development of virtual subjects to explore variability and uncertainty in the represented biology, and (iii) simulations of virtual populations for different interventions. gQSPSim works with SimBiology‐built models using components such as species, doses, variants, and rules. All functionalities are equipped with an interactive visualization interface and the ability to generate presentation‐ready figures. In addition, standardized gQSPSim sessions can be shared and saved for future extension and reuse. In this work, we demonstrate gQSPSim’s capabilities with a standard target‐mediated drug disposition model and a published model of anti‐proprotein convertase subtilisin/kexin type 9 (PCSK9) treatment of hypercholesterolemia.

Despite considerable investment into potential therapeutic approaches for Alzheimer's disease (AD), currently approved treatment options are limited. Predictive modeling using quantitative systems pharmacology (QSP) can be used to guide the design of clinical trials in AD. This study developed a QSP model representing amyloid beta (Aβ) pathophysiology in AD. The model included mechanisms of Aβ monomer production and aggregation to form insoluble fibrils and plaques; the transport of soluble species between the compartments of brain, cerebrospinal fluid (CSF), and plasma; and the pharmacokinetics, transport, and binding of monoclonal antibodies to targets in the three compartments. Ordinary differential equations were used to describe these processes quantitatively. The model components were calibrated to data from the literature and internal studies, including quantitative data supporting the underlying AD biology and clinical data from clinical trials for anti‐Aβ monoclonal antibodies (mAbs) aducanumab, crenezumab, gantenerumab, and solanezumab. The model was developed for an apolipoprotein E (APOE) ɛ4 allele carrier and tested for an APOE ɛ4 noncarrier. Results indicate that the model is consistent with data on clinical Aβ accumulation in untreated individuals and those treated with monoclonal antibodies, capturing increases in Aβ load accurately. This model may be used to investigate additional AD mechanisms and their impact on biomarkers, as well as predict Aβ load at different dose levels for mAbs with known targets and binding affinities. This model may facilitate the design of scientifically enriched and efficient clinical trials by enabling a priori prediction of biomarker dynamics in the brain and CSF.

The heterogeneous polyclonal nature of multiple myeloma complicates the identification of protein biomarkers predictive of drug response. In this study, a pharmacodynamic systems modeling approach was used to link in vitro bortezomib exposure and myeloma cell death. The exposure‐response was integrated through a network of important protein biomarker dynamics activated by bortezomib in four myeloma cell lines. The pharmacodynamic models reasonably characterized the protein and myeloma cell dynamics simultaneously following bortezomib (20 nM) treatment. The models were used to identify differences in pathway dynamics across cell lines from model‐estimated protein biomarker turnover parameters and global sensitivity analyses. Additionally, a statistical correlation analysis between drug sensitivity and model‐fitted protein activation profiles (i.e., cumulative area under the protein expression‐time curves) supported the identification of shared biomarkers associated with sensitivity differences among the cell lines. Both types of analysis identified similar important proteins associated with bortezomib pharmacodynamics, such as phosphorylated Nuclear Factor kappa‐light‐chain‐enhancer of activated B cells (pNFkappaB), phosphorylated protein kinase B (pAKT), and caspase‐8 (Cas 8).

INTRODUCTION Semorinemab, an anti‐tau monoclonal antibody, was assessed in two Phase II trials for Alzheimer's disease (AD). Plasma and cerebrospinal fluid (CSF) biomarkers provided insights into the drug's potential mechanism of action. METHODS Qualified assays were used to measure biomarkers of tau, amyloidosis, glial activity, neuroinflammation, synaptic function, and neurodegeneration from participant samples in Tauriel (NCT03289143) and Lauriet (NCT03828747) Phase II trials. RESULTS Plasma phosphorylated Tau 181 (pTau181) and CSF chitinase‐3‐like protein 1 (YKL‐40) increased following semorinemab treatment in both studies. In Lauriet, increasing plasma glial fibrillary protein (GFAP) concentrations stabilized with semorinemab, while this was not observed in Tauriel. Other AD pathophysiology biomarkers showed no consistent response to semorinemab. DISCUSSION Increases in CSF YKL‐40 suggest that semorinemab may stimulate microglia activation in the presence of AD‐associated Tau pathology, but not in healthy controls. Stabilization of plasma GFAP in Lauriet indicates a possible impact on reactive gliosis in mild‐to‐moderate AD. Trial Registration: Tauriel ClinicalTrials.gov Identifier: NCT03289143. Lauriet ClinicalTrials.gov Identifier: NCT03828747. Phase 1 ClinicalTrials.gov Identifier: NCT02820896. Highlights AD pathophysiology biomarkers were measured to assess the mechanism of action. Semorinemab increased CSF YKL‐40 in participants with AD but not in healthy controls. Semorinemab possibly stabilized plasma GFAP in the Lauriet trial. Semorinemab treatment may activate microglia and moderate reactive gliosis.

Multiple myeloma is a complex heterogeneous hematological malignancy comprising of rapidly proliferating genetically diverse clonal plasma cells. Tumor initiation, development, and progression occur through a series of pre-malignant stages, with sequential acquisition of genomic aberrations. The developing genomic landscape offers opportunity for microenvironment, drug treatment, and immune system related selection pressures to introduce various somatic mutations providing a fitness advantage to cell populations and leading to a branching evolutionary scheme of clonal evolution. Clonal evolution—a process that mimics Darwinian evolution—enables coexistence of heterogeneous clonal and sub-clonal populations providing myeloma cells the ability to acclimatize and grow as the disease progresses. The inter-clonal heterogeneity is the primary cause for variable responses to drug treatment between patients and within the same patient at different times. The prevalence of specific sub-clones over others results in recurring cycles of remission and relapse in patients, eventually leading to refractory relapse. This therapeutic challenge necessitates the need to study the determinants of heterogeneous responses in myeloma patients to monitor disease progression, via prognostic biomarkers, and devise strategies for the implementation of personalized precision medicine. The purpose of the work presented in this dissertation is to investigate the determinants of heterogeneity in drug treatment response of molecularly different in vitro myeloma cell lines representing clonal populations of cells. This research employs a quantitative systems pharmacology approach to explore the heterogeneous intracellular signaling mechanisms governing drug action in myeloma cells and to identify common protein biomarkers associated with differing drug sensitivities as useful tools for translatable patient risk stratification and response prediction. A genetically diverse panel of four multiple myeloma cell lines (i.e., U266, RPMI8226, MM.1S, and NCI-H929) were examined for pharmacodynamic differences in response to treatment with bortezomib, a proteasome inhibitor commonly used for the treatment of multiple myeloma. Heterogeneity in response was quantitatively established via concentration-effect and cell proliferation dynamical studies. Of the four cell lines, MM.1S and NCI-H929 were found to be more sensitive to bortezomib treatment in comparison to U266 and RPMI8226. Traditional pharmacokinetic-pharmacodynamic modeling of literature reported in vivo xenograft data of the cell lines also confirmed sensitivity differences to bortezomib among the cell lines (Chapter 2). In order to test the hypothesis that differences in drug-induced downstream intracellular protein signaling is the basis for response heterogeneity, a comprehensive logic-based Boolean network model was developed, comprising of 97 nodes and 202 edges, to characterize the intracellular protein signaling governing cell growth, proliferation, and apoptosis in myeloma cells. The network was used to simulate protein dynamics under bortezomib perturbation to examine the mechanisms of bortezomib induced apoptosis in myeloma cells. Interestingly, simulations identified the activation of both intrinsic and extrinsic pathways of apoptosis in myeloma cells. Key proteins central to the network and relevant to the pharmacodynamics of the drug were revealed by topology based centrality measures and a model reduction algorithm (Chapter 3). Network-guided time-course dynamics of ten protein biomarkers, namely, pNFκB, pAKT, pmTOR, Bcl-2, pJNK, pp53, p21, pBAD, Caspase 8, and Caspase 9, were characterized in untreated-control, 2 nM bortezomib (only RPMI8226 and MM.1S cells), and 20 nM bortezomib treated U266, RPMI8226, MM.1S, and NCI-H929 cells using the fluorescence intensity based MAGPIX® protein assay system. Broadly, a greater control-normalized expression of signaling proteins was observed in the more sensitive MM.1S and NCI-H929 cells in comparison to U266 and RPMI8226 cells. Also, the onset of activation of these proteins was faster in the more sensitive cell lines. The lower drug concentration (2 nM) showed a slower onset and lower magnitude of expression in sensitive MM.1S cells and did not induce a change in expression of proteins in RPMI8226 cells. Overall, the experimental analysis suggested an association between intracellular protein signaling dynamics and drug sensitivity (Chapter 4). (Abstract shortened by ProQuest.)