Multiscale mapping of transcriptomic signatures for cardiotoxic drugs.

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

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.

Telemedicine platforms must be leveraged to strengthen rural health systems.

Benefit of telehealth goes beyond providing consultations. Telehealth can be used to enhance rural workforce capabilities and scope of practice as part of strengthening rural health systems.

Survey of brachytherapy training experience among radiation oncology trainees and fellows in the Royal Australian and New Zealand College of Radiologists (RANZCR).

INTRODUCTION: To evaluate brachytherapy training experience among trainees and fellows trained through the Royal Australian and New Zealand College of Radiologists (RANZCR). METHODS: All current trainees and fellows (who obtained fellowship from 2015 onwards) were sent an online anonymous questionnaire on various aspects of brachytherapy training, including number of cases observed/ performed, opinions on brachytherapy assessment during training, barriers to brachytherapy training and future role of brachytherapy. RESULTS: The overall survey response rate was 24% (40/161 trainees, 30/126 fellows). Of the 70 respondents, 50 (71%), 38 (54%) and 43 (61%) reported to have received formal brachytherapy teaching from radiation oncologists, radiation therapists and medical physicists respectively. Most respondents had exposure to gynaecology brachytherapy - two-thirds of trainees and all fellows have performed at least one gynaecology brachytherapy procedure. Prostate brachytherapy exposure was more limited - by the end of training, 27% and 13% of fellows did not have exposure to LDR and HDR prostate brachytherapy. More than two-thirds indicated there should be a minimum number of brachytherapy case requirements during training, and half indicated that trainees should be involved in ≥6 gynaecology brachytherapy procedures. Barriers affecting training include lack of caseload (70%) and perceived decreasing role of brachytherapy (66%). Forty-three percent of respondents were concerned about the decline in brachytherapy utilisation. CONCLUSION: This is the first survey on brachytherapy training experience among RANZCR trainees and fellows. It highlighted limited brachytherapy exposure during RANZCR training, and the need to revisit brachytherapy training requirement in the current training programme, along with long-term brachytherapy workforce planning.

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.

Outcomes of stage I/II follicular lymphoma in the PET era: an international study from the Australian Lymphoma Alliance.

Management practices in early-stage (I/II) follicular lymphoma (FL) are variable and include radiation (RT), systemic therapy, or combined modality therapy (CMT). There is a paucity of data regarding maintenance rituximab in this cohort. We conducted an international retrospective study of patients with newly diagnosed early-stage FL staged with positron emission tomography (PET)-computed tomography and bone marrow biopsy. Three hundred sixty-five patients (stage I, n = 221), median age 63 years, treated from 2005-2017 were included, with a median follow-up of 45 months. Management included watchful waiting (WW; n = 85) and active treatment (n = 280). The latter consisted of RT alone (n = 171) or systemic therapy (immunochemotherapy [n = 63] or CMT [n = 46]). Forty-nine systemically treated patients received maintenance rituximab; 72.7% of stage I patients received RT alone, compared to 42.6% with stage II (P < .001). Active therapies yielded comparable overall response rates (P = .87). RT alone and systemic therapy without maintenance rituximab yielded similar progression-free survival (PFS) (hazard ratio [HR], 1.32; 95% confidence interval [CI], 0.77-2.34; P = .96). Maintenance rituximab improved PFS (HR, 0.24; 95% CI, 0.095-0.64; P = .017). The incidence of transformation was lower with systemic therapy compared to RT or WW (HR, 0.20; 95% CI, 0.070-0.61; P = .034). Overall survival was similar among all practices, including WW (P = .40). In the largest comparative assessment of management practices in the modern era, variable practices each resulted in similar excellent outcomes. Randomized studies are required to determine the optimal treatment in early-stage FL.

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.

Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses.

The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop transcriptomic signatures for specific drugs. We analyzed lung adenocarcinoma patient data from The Cancer Genome Atlas and identified a subset of patients in which xanthine dehydrogenase (XDH) expression correlated with decreased survival. We tested allopurinol, an FDA-approved drug that inhibits XDH, on human non-small-cell lung cancer (NSCLC) cell lines obtained from the Broad Institute Cancer Cell Line Encyclopedia and identified sensitive and resistant cell lines. We utilized the transcriptomic profiles of these cell lines to identify six-gene signatures for allopurinol-sensitive and allopurinol-resistant cell lines. Transcriptomic networks identified JAK2 as an additional target in allopurinol-resistant lines. Treatment of resistant cell lines with allopurinol and CEP-33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP-33779 was verified in vivo using tumor formation in NCR-nude mice. We utilized the six-gene signatures to predict five additional allopurinol-sensitive NSCLC cell lines and four allopurinol-resistant cell lines susceptible to combination therapy. We searched the transcriptomic data from a library of patient-derived NSCLC tumors from the Jackson Laboratory to identify tumors that would be predicted to be sensitive to allopurinol or allopurinol + CEP-33779 treatment. Patient-derived tumors showed the predicted drug sensitivity in vivo. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.

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.

A severe transmissible Majocchi's granuloma in an immunocompetent returned traveler.

Severe dermatophyte infection is rare in immunocompetent adults. Recently cases have been described in travelers returning from South East Asia (Luchsinger et al., 2015) [1]. These may be sexually transmitted and can have permanent sequelae. We describe the first reported case of Majocchi's granuloma (MG) in an Australian returned traveler and its subsequent transmission via sexual contact. Both patients were successfully treated with systemic antifungals. MG should be considered in patients with severe rash after travel to South East Asia.

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.