Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions.

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte- like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions.

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

Cell state-directed therapy - epigenetic modulation of gene transcription demonstrated with a quantitative systems pharmacology model of temozolomide.

Cancer therapy continues to be plagued by modest therapeutic advances. This is particularly evident in glioblastoma multiforme (GBM) wherein treatment failures are attributed to intratumoral heterogeneity (ITH), a dynamic process of cell state transitions or plasticity. To address ITH, we introduce the concept of cell state-directed (CSD) therapy through a quantitative systems pharmacology model of temozolomide (TMZ), a cornerstone of GBM drug therapy. The model consisting of multiple modules incorporated an epigenetic-based gene transcription-translation module that enabled CSD therapy. Numerous model simulations were conducted to demonstrate the potential impact of CSD therapy on TMZ activity. The simulations included those based on global sensitivity analyses to identify fragile nodes - MDM2 and XIAP - in the network, and also how an epigenetic modifier (birabresib) could overcome a mechanism of TMZ resistance. The positive results of CSD therapy on TMZ activity supports continued efforts to develop CSD therapy as a new anticancer approach.

Educational Needs for Quantitative Systems Pharmacology Scientists.

There is a demand for scientists trained in quantitative systems pharmacology (QSP) that has yet to be met by changes in graduate education. The multidisciplinary nature of QSP is not unlike its predecessor, pharmacokinetics (PKs) and pharmacodynamics (PDs) that have now become firmly established in many educational programs. A hindrance to the evolution of educational programs for QSP is explored and suggestions to move QSP into its proper position as a unique discipline are presented.

Epigenetic instability may alter cell state transitions and anticancer drug resistance.

Drug resistance is a significant obstacle to successful and durable anti-cancer therapy. Targeted therapy is often effective during early phases of treatment; however, eventually cancer cells adapt and transition to drug-resistant cells states rendering the treatment ineffective. It is proposed that cell state can be a determinant of drug efficacy and manipulated to affect the development of anticancer drug resistance. In this work, we developed two stochastic cell state models and an integrated stochastic-deterministic model referenced to brain tumors. The stochastic cell state models included transcriptionally-permissive and -restrictive states based on the underlying hypothesis that epigenetic instability mitigates lock-in of drug-resistant states. When moderate epigenetic instability was implemented the drug-resistant cell populations were reduced, on average, by 60%, whereas a high level of epigenetic disruption reduced them by about 90%. The stochastic-deterministic model utilized the stochastic cell state model to drive the dynamics of the DNA repair enzyme, methylguanine-methyltransferase (MGMT), that repairs temozolomide (TMZ)-induced O6-methylguanine (O6mG) adducts. In the presence of epigenetic instability, the production of MGMT decreased that coincided with an increase of O6mG adducts following a multiple-dose regimen of TMZ. Generation of epigenetic instability via epigenetic modifier therapy could be a viable strategy to mitigate anticancer drug resistance.

Inhibition of HIPK2 Alleviates Thoracic Aortic Disease in Mice With Progressively Severe Marfan Syndrome.

Objective: Despite considerable research, the goal of finding nonsurgical remedies against thoracic aortic aneurysm and acute aortic dissection remains elusive. We sought to identify a novel aortic PK (protein kinase) that can be pharmacologically targeted to mitigate aneurysmal disease in a well-established mouse model of early-onset progressively severe Marfan syndrome (MFS). Approach and Results: Computational analyses of transcriptomic data derived from the ascending aorta of MFS mice predicted a probable association between thoracic aortic aneurysm and acute aortic dissection development and the multifunctional, stress-activated HIPK2 (homeodomain-interacting protein kinase 2). Consistent with this prediction, Hipk2 gene inactivation significantly extended the survival of MFS mice by slowing aneurysm growth and delaying transmural rupture. HIPK2 also ranked among the top predicted PKs in computational analyses of DEGs (differentially expressed genes) in the dilated aorta of 3 MFS patients, which strengthened the clinical relevance of the experimental finding. Additional in silico analyses of the human and mouse data sets identified the TGF (transforming growth factor)-ß/Smad3 signaling pathway as a potential target of HIPK2 in the MFS aorta. Chronic treatment of MFS mice with an allosteric inhibitor of HIPK2-mediated stimulation of Smad3 signaling validated this prediction by mitigating thoracic aortic aneurysm and acute aortic dissection pathology and partially improving aortic material stiffness. Conclusions: HIPK2 is a previously unrecognized determinant of aneurysmal disease and an attractive new target for antithoracic aortic aneurysm and acute aortic dissection multidrug therapy.

Hybrid physiologically-based pharmacokinetic model for remdesivir: Application to SARS-CoV-2.

A novel coronavirus, severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) or coronavirus disease 2019 (COVID-19), has caused a pandemic that continues to cause catastrophic health and economic carnage and has escalated the identification and development of antiviral agents. Remdesivir (RDV), a prodrug and requires intracellular conversions to the active triphosphate nucleoside (TN) has surfaced as an active anti-SARS-CoV-2 drug. To properly design therapeutic treatment regimens, it is imperative to determine if adequate intracellular TN concentrations are achieved in target tissues, such as the lungs. Because measurement of such concentrations is unrealistic in patients, a physiologically-based pharmacokinetic (PBPK) model was developed to characterize RDV and TN disposition. Specifically, a hybrid PBPK model was developed based on previously reported data in humans. The model represented each tissue as a two-compartment model-both extracellular and intracellular compartment wherein each intracellular compartment contained a comprehensive metabolic model to the ultimate active metabolite TN. Global sensitivity analyses and Monte-Carlo simulations were conducted to assess which parameters and how highly sensitive ones impacted peripheral blood mononuclear cells and intracellular lung TN profiles. Finally, clinical multiple-dose regimens indicated that minimum lung intracellular TN concentrations ranged from ~ 9 uM to 4 uM, which suggest current regimens are effective based on in vitro half-maximal effective concentration values. The model can be used to explore tissue drug disposition under various conditions and regimens, and expanded to pharmacodynamic models.

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity.

Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood, and its prediction remains challenging. We obtain transcriptional profiles of human heart-derived primary cardiomyocyte like cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with the cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.

Predicting In Vivo Efficacy from In Vitro Data: Quantitative Systems Pharmacology Modeling for an Epigenetic Modifier Drug in Cancer.

Reliably predicting in vivo efficacy from in vitro data would facilitate drug development by reducing animal usage and guiding drug dosing in human clinical trials. However, such prediction remains challenging. Here, we built a quantitative pharmacokinetic/pharmacodynamic (PK/PD) mathematical model capable of predicting in vivo efficacy in animal xenograft models of tumor growth while trained almost exclusively on in vitro cell culture data sets. We studied a chemical inhibitor of LSD1 (ORY-1001), a lysine-specific histone demethylase enzyme with epigenetic function, and drug-induced regulation of target engagement, biomarker levels, and tumor cell growth across multiple doses administered in a pulsed and continuous fashion. A PK model of unbound plasma drug concentration was linked to the in vitro PD model, which enabled the prediction of in vivo tumor growth dynamics across a range of drug doses and regimens. Remarkably, only a change in a single parameter-the one controlling intrinsic cell/tumor growth in the absence of drug-was needed to scale the PD model from the in vitro to in vivo setting. These findings create a framework for using in vitro data to predict in vivo drug efficacy with clear benefits to reducing animal usage while enabling the collection of dense time course and dose response data in a highly controlled in vitro environment.

Systems pharmacology-based integration of human and mouse data for drug repurposing to treat thoracic aneurysms.

Marfan syndrome (MFS) is associated with mutations in fibrillin-1 that predispose afflicted individuals to progressive thoracic aortic aneurysm (TAA) leading to dissection and rupture of the vessel wall. Here we combined computational and experimental approaches to identify and test FDA-approved drugs that may slow or even halt aneurysm progression. Computational analyses of transcriptomic data derived from the aortas of MFS patients and MFS mice (Fbn1mgR/mgR mice) predicted that subcellular pathways associated with reduced muscle contractility are key TAA determinants that could be targeted with the GABAB receptor agonist baclofen. Systemic administration of baclofen to Fbn1mgR/mgR mice validated our computational prediction by mitigating arterial disease progression at the cellular and physiological levels. Interestingly, baclofen improved muscle contraction-related subcellular pathways by upregulating a different set of genes than those downregulated in the aorta of vehicle-treated Fbn1mgR/mgR mice. Distinct transcriptomic profiles were also associated with drug-treated MFS and wild-type mice. Thus, systems pharmacology approaches that compare patient- and mouse-derived transcriptomic data for subcellular pathway-based drug repurposing represent an effective strategy to identify potential new treatments of human diseases.

CNS Anticancer Drug Discovery and Development: 2016 conference insights.

CNS Anticancer Drug Discovery and Development, 16-17 November 2016, Scottsdale, AZ, USA The 2016 second CNS Anticancer Drug Discovery and Development Conference addressed diverse viewpoints about why new drug discovery/development focused on CNS cancers has been sorely lacking. Despite more than 70,000 individuals in the USA being diagnosed with a primary brain malignancy and 151,669-286,486 suffering from metastatic CNS cancer, in 1999, temozolomide was the last drug approved by the US FDA as an anticancer agent for high-grade gliomas. Among the topics discussed were economic factors and pharmaceutical risk assessments, regulatory constraints and perceptions and the need for improved imaging surrogates of drug activity. Included were modeling tumor growth and drug effects in a medical environment in which direct tumor sampling for biological effects can be problematic, potential new drugs under investigation and targets for drug discovery and development. The long trajectory and diverse impediments to novel drug discovery, and expectation that more than one drug will be needed to adequately inhibit critical intracellular tumor pathways were viewed as major disincentives for most pharmaceutical/biotechnology companies. While there were a few unanimities, one consensus is the need for continued and focused discussion among academic and industry scientists and clinicians to address tumor targets, new drug chemistry, and more time- and cost-efficient clinical trials based on surrogate end points.

Intracerebral Distribution of the Oncometabolite d-2-Hydroxyglutarate in Mice Bearing Mutant Isocitrate Dehydrogenase Brain Tumors: Implications for Tumorigenesis.

The prevalence of mutant isocitrate dehydrogenase 1 (IDH1) brain tumors has generated significant efforts to understand the role of the mutated enzyme product d-2-hydroxyglutarate (D2HG), an oncometabolite, in tumorigenesis, as well as means to eliminate it. Glymphatic clearance was proposed as a pathway that could be manipulated to accelerate D2HG clearance and dictated the study design that consisted of two cohorts of mice bearing U87/mutant IDH1 intracerebral tumors that underwent two microdialysis - providing D2HG interstitial fluid concentrations - sampling periods of awake and asleep (activate glymphatic clearance) in a crossover manner. Glymphatic clearance was found not to have a significant effect on D2HG brain tumor interstitial fluid concentrations that were 126.9 ± 74.8 µM awake and 117.6 ± 98.6 µM asleep. These concentrations, although low relative to total brain tumor concentrations of 6.8 ± 3.6 mM, were considered sufficient to be transported by interstitial fluid and taken up into normal cells to cause deleterious effects. A model of D2HG CNS distribution supported this contention and was further supported by in vitro studies that showed D2HG could interfere with immune cell function. The study provides insight into the compartmental distribution of D2HG in the brain, wherein the interstitial fluid serves as a dynamic pathway for D2HG to enter normal cells and contribute to tumorigenesis.

Histone Methylation by Temozolomide; A Classic DNA Methylating Anticancer Drug.

BACKGROUND/AIM: The alkylating agent, temozolomide (TMZ), is considered the standard-of-care for high-grade astrocytomas -known as glioblastoma multiforme (GBM)- an aggressive type of tumor with poor prognosis. The therapeutic benefit of TMZ is attributed to formation of DNA adducts involving the methylation of purine bases in DNA. We investigated the effects of TMZ on arginine and lysine amino acids, histone H3 peptides and histone H3 proteins. MATERIALS AND METHODS: Chemical modification of amino acids, histone H3 peptide and protein by TMZ was performed in phosphate buffer at physiological pH. The reaction products were examined by mass spectrometry and western blot analysis. RESULTS: Our results showed that TMZ following conversion to a methylating cation, can methylate histone H3 peptide and histone H3 protein, suggesting that TMZ exerts its anticancer activity not only through its interaction with DNA, but also through alterations of protein post-translational modifications. CONCLUSION: The possibility that TMZ can methylate histones involved with epigenetic regulation of protein indicates a potentially unique mechanism of action. The study will contribute to the understanding the anticancer activity of TMZ in order to develop novel targeted molecular strategies to advance the cancer treatment.

CNS Anticancer Drug Discovery and Development Conference White Paper.

Following the first CNS Anticancer Drug Discovery and Development Conference, the speakers from the first 4 sessions and organizers of the conference created this White Paper hoping to stimulate more and better CNS anticancer drug discovery and development. The first part of the White Paper reviews, comments, and, in some cases, expands on the 4 session areas critical to new drug development: pharmacological challenges, recent drug approaches, drug targets and discovery, and clinical paths. Following this concise review of the science and clinical aspects of new CNS anticancer drug discovery and development, we discuss, under the rubric "Accelerating Drug Discovery and Development for Brain Tumors," further reasons why the pharmaceutical industry and academia have failed to develop new anticancer drugs for CNS malignancies and what it will take to change the current status quo and develop the drugs so desperately needed by our patients with malignant CNS tumors. While this White Paper is not a formal roadmap to that end, it should be an educational guide to clinicians and scientists to help move a stagnant field forward.

Network pharmacodynamic models for customized cancer therapy.

Pharmacokinetics (PKs) and pharmacodynamics (PDs) have always been integral to the design of rational drug dosing regimens. Early on PK-driven approaches came under the auspices of therapeutic drug monitoring that progressed into population-based PK and PK/PD modeling analyses. As the availability of tissue samples for measurement of drug concentrations is limited in patients, the bulk of such model-based methods relied on plasma drug concentrations to both build models and monitor therapy. The continued advances in systems biology and the spawning of systems pharmacology propelled the creation of enhanced PD (ePD) models. One of the main characteristic of ePD models is that they are derived from mechanistically grounded biochemical reaction networks. These models are commonly represented as systems of coupled ordinary differential equations with the ability to tailor each reaction and protein concentration to an individual's genomic/proteomic profile. As patient genomic analyses become more common, many genetic and protein abnormalities can be represented in the ePD models, and thus offer a path toward personalized anticancer therapies. By linking PK models to ePD models, a full spectrum of pharmacological simulation tools is available to design sophisticated multidrug regimens. However, ePD models are not a panacea and face challenges in model identifiability, scaling and parameter estimation. Nonetheless, as new technologies evolve and are coupled with fresh ideas on model implementation, it is likely that ePD and PK/ePD models will be considered a viable enterprise to customize anticancer drug therapy.

Inhibition of cancer-associated mutant isocitrate dehydrogenases: synthesis, structure-activity relationship, and selective antitumor activity.

Mutations of isocitrate dehydrogenase 1 (IDH1) are frequently found in certain cancers such as glioma. Different from the wild-type (WT) IDH1, the mutant enzymes catalyze the reduction of α-ketoglutaric acid to d-2-hydroxyglutaric acid (D2HG), leading to cancer initiation. Several 1-hydroxypyridin-2-one compounds were identified to be inhibitors of IDH1(R132H). A total of 61 derivatives were synthesized, and their structure-activity relationships were investigated. Potent IDH1(R132H) inhibitors were identified with Ki values as low as 140 nM, while they possess weak or no activity against WT IDH1. Activities of selected compounds against IDH1(R132C) were found to be correlated with their inhibitory activities against IDH1(R132H), as well as cellular production of D2HG, with R(2) of 0.83 and 0.73, respectively. Several inhibitors were found to be permeable through the blood-brain barrier in a cell-based model assay and exhibit potent and selective activity (EC50 = 0.26-1.8 µM) against glioma cells with the IDH1 R132H mutation.

Preclinical pharmacological evaluation of a novel multiple kinase inhibitor, ON123300, in brain tumor models.

ON123300 is a low molecular weight multikinase inhibitor identified through a series of screens that supported further analyses for brain tumor chemotherapy. Biochemical assays indicated that ON123300 was a strong inhibitor of Ark5 and CDK4, as well as growth factor receptor tyrosine kinases such as ß-type platelet-derived growth factor receptor (PDGFRß). ON123300 inhibited U87 glioma cell proliferation with an IC(50) 3.4 ± 0.1 µmol/L and reduced phosphorylation of Akt, yet it also unexpectedly induced Erk activation, both in a dose- and time-dependent manner that subsequently was attributed to relieving Akt-mediated C-Raf S259 inactivation and activating a p70S6K-initiated PI3K-negative feedback loop. Cotreatment with the EGFR inhibitor gefitinib produced synergistic cytotoxic effects. Pursuant to the in vitro studies, in vivo pharmacokinetic and pharmacodynamic studies of ON123300 were completed in mice bearing intracerebral U87 tumors following intravenous doses of 5 and 25 mg/kg alone, and also at the higher dose concurrently with gefitinib. ON123300 showed high brain and brain tumor accumulation based on brain partition coefficient values of at least 2.5. Consistent with the in vitro studies, single agent ON123300 caused a dose-dependent suppression of phosphorylation of Akt as well as activation of Erk in brain tumors, whereas addition of gefitinib to the ON123300 regimen significantly enhanced p-Akt inhibition and prevented Erk activation. In summary, ON123300 demonstrated favorable pharmacokinetic characteristics, and future development for brain tumor therapy would require use of combinations, such as gefitinib, that mitigate its Erk activation and enhance its activity.

The effect of ABCG2 and ABCC4 on the pharmacokinetics of methotrexate in the brain.

Methotrexate (MTX) is the cornerstone of chemotherapy for primary central nervous system lymphoma, yet how the blood-brain barrier (BBB) efflux transporters ABCG2 and ABCC4 influence the required high-dose therapy is unknown. To evaluate their role, we used four mouse strains, C57BL/6 (wild-type; WT), Abcg2(-/-), Abcc4(-/-), and Abcg2(-/-);Abcc4(-/-) (double knockout; DKO) to conduct brain microdialysis studies after single intravenous MTX doses of 50 mg/kg. When the area under the concentration-time curve for plasma (AUC(plasma)) was used to assess systemic exposure to MTX, the rank order was Abcc4(-/-) < WT < Abcg2(-/-) < Abcg2(-/-)Abcc4(-/-). Only the DKO exposure was significantly higher than that of the WT group (P < 0.01), a reflection of the role of Abcg2 in biliary excretion and Abcc4 in renal excretion. MTX brain interstitial fluid concentrations obtained by microdialysis were used to calculate the area under the concentration-time curve for the brain (AUC(brain)), which found the rank order of exposure to be WT < Abcc4(-/-) < Abcg2(-/-) < Abcg2(-/-)Abcc4(-/-) with the largest difference being 4-fold: 286.13 ± 130 µg*min/ml (DKO) versus 66.85 ± 26 (WT). Because the transporters affected the systemic disposition of MTX, particularly in the DKO group, the ratio of the AUC(brain)/AUC(plasma) or the brain/plasma partition coefficient Kp was calculated, revealing that the DKO strain had a significantly higher value (0.23 ± 0.09) than the WT strain (0.11 ± 0.05). Both Abcg2 and Abcc4 limited BBB penetration of MTX; however, only when both drug efflux pumps were negated did the brain accumulation of MTX significantly increase. These findings indicate a contributory role of both ABCG2 and ABCC4 to limiting MTX distribution in patients.