Concentration-QT analysis of the randomized, placebo- and moxifloxacin-controlled thorough QT study of umeclidinium monotherapy and umeclidinium/vilanterol combination in healthy subjects.

The long-acting muscarinic antagonist umeclidinium (UMEC) is approved as a once-daily monotherapy and in combination with the long-acting ß2 agonist vilanterol (VI) for chronic obstructive pulmonary disease. The objective of this analysis was to assess the relationship between observed plasma UMEC and/or VI concentrations and QT interval corrected using Fridericia's correction (QTcF). 103 subjects were enrolled and 86 (83 %) completed the study. Subjects were randomized to 4 of 5 repeat-dose treatments (days 1-10: n = 77 subjects received placebo, n = 76 UMEC 500 µg, n = 78 UMEC/VI 125/25 µg, or n = 76 UMEC/VI 500/100 µg; day 10: n = 74 oral tablet moxifloxacin 400 mg [positive control]). The concentration-QTcF interval relationship was examined using nonlinear mixed-effects methods. For UMEC, predicted QTcF interval prolongation (at observed geometric mean of maximum plasma concentrations) was -2.38 ms (90 % prediction interval [PI] -3.82, -0.85) with UMEC 500 µg and -0.50 ms (90 % PI -0.80, -0.18) and -2.01 ms (90 % PI -3.22, -0.72) with UMEC/VI 125/25 µg and 500/100 µg, respectively. For VI, estimates were 5.89 ms (90 % PI 4.89, 6.91) and 7.23 ms (90 % PI 5.88, 8.55) with UMEC/VI 125/25 µg and 500/100 µg, respectively. Combined additive mean effects were estimated for UMEC/VI 125/25 µg (5.39 ms [90 % PI 4.40, 6.47]) and 500/100 µg (5.22 ms [90 % PI 3.72, 6.80]). The model-predicted decrease with UMEC and increase with UMEC/VI combination in QTcF interval suggest that the QT effect is likely attributable to VI. These model-predicted results support those of previously-published traditional statistical analyses.

The complete genome sequence of Francisella tularensis, the causative agent of tularemia.

Francisella tularensis is one of the most infectious human pathogens known. In the past, both the former Soviet Union and the US had programs to develop weapons containing the bacterium. We report the complete genome sequence of a highly virulent isolate of F. tularensis (1,892,819 bp). The sequence uncovers previously uncharacterized genes encoding type IV pili, a surface polysaccharide and iron-acquisition systems. Several virulence-associated genes were located in a putative pathogenicity island, which was duplicated in the genome. More than 10% of the putative coding sequences contained insertion-deletion or substitution mutations and seemed to be deteriorating. The genome is rich in IS elements, including IS630 Tc-1 mariner family transposons, which are not expected in a prokaryote. We used a computational method for predicting metabolic pathways and found an unexpectedly high proportion of disrupted pathways, explaining the fastidious nutritional requirements of the bacterium. The loss of biosynthetic pathways indicates that F. tularensis is an obligate host-dependent bacterium in its natural life cycle. Our results have implications for our understanding of how highly virulent human pathogens evolve and will expedite strategies to combat them.

Application of systems biology to identify pharmacological mechanisms of thrombotic microangiopathy evoked by combined activated prothrombin complex concentrate and emicizumab.

Emicizumab is a bispecific monoclonal antibody that substitutes for the function of missing or deficient factor VIII (FVIII) in people with hemophilia A (PwHA). Long-term safety and efficacy of emicizumab have been demonstrated in several clinical trials. Nevertheless, in the first of these, three cases of thrombotic microangiopathy (TMA) occurred in PwHA treated with emicizumab receiving high doses of activated prothrombin complex concentrate (aPCC), a bypassing agent used for treating breakthrough bleeds when FVIII neutralizing antibodies (inhibitors) make FVIII replacement ineffective. The aim of the present work is to offer a method to elucidate the pathophysiological and pharmacological mechanisms involved in this treatment-induced TMA. Systems biology and machine learning-based Therapeutic Performance Mapping System is a validated in silico technology that allowed us to construct models of potential mechanisms behind induced TMA. Two drug combinations were modeled and assessed: emicizumab plus aPCC and emicizumab plus recombinant activated factor VII (another bypassing agent). Our models showed that both combinations were related to activation of the coagulation cascade. However, mechanisms involved mainly in platelet activation and possibly in complement activation were detected only for emicizumab plus aPCC, potentially explaining the occurrence of TMA only in this combination.

De-risking clinical trial failure through mechanistic simulation.

Drug development typically comprises a combination of pre-clinical experimentation, clinical trials, and statistical data-driven analyses. Therapeutic failure in late-stage clinical development costs the pharmaceutical industry billions of USD per year. Clinical trial simulation represents a key derisking strategy and combining them with mechanistic models allows one to test hypotheses for mechanisms of failure and to improve trial designs. This is illustrated with a T-cell activation model, used to simulate the clinical trials of IMA901, a short-peptide cancer vaccine. Simulation results were consistent with observed outcomes and predicted that responses are limited by peptide off-rates, peptide competition for dendritic cell (DC) binding, and DC migration times. These insights were used to hypothesise alternate trial designs predicted to improve efficacy outcomes. This framework illustrates how mechanistic models can complement clinical, experimental, and data-driven studies to understand, test, and improve trial designs, and how results may differ between humans and mice.

Quantifying the limits of CAR T-cell delivery in mice and men.

CAR (Chimeric Antigen Receptor) T cells have demonstrated clinical success for the treatment of multiple lymphomas and leukaemias, but not for various solid tumours, despite promising data from murine models. Lower effective CAR T-cell delivery rates to human solid tumours compared to haematological malignancies in humans and solid tumours in mice might partially explain these divergent outcomes. We used anatomical and physiological data for human and rodent circulatory systems to calculate the typical perfusion of healthy and tumour tissues, and estimated the upper limits of immune cell delivery rates across different organs, tumour types and species. Estimated maximum delivery rates were up to 10 000-fold greater in mice than humans yet reported CAR T-cell doses are typically only 10-100-fold lower in mice, suggesting that the effective delivery rates of CAR T cells into tumours in clinical trials are far lower than in corresponding mouse models. Estimated delivery rates were found to be consistent with published positron emission tomography data. Results suggest that higher effective human doses may be needed to drive efficacy comparable to mouse solid tumour models, and that lower doses should be tested in mice. We posit that quantitation of species and organ-specific delivery and homing of engineered T cells will be key to unlocking their potential for solid tumours.

Predicted limited redistribution of T cells to secondary lymphoid tissue correlates with increased risk of haematological malignancies in asplenic patients.

The spleen, a secondary lymphoid tissue (SLT), has an important role in generation of adaptive immune responses. Although splenectomy remains a common procedure, recent studies reported poor prognosis and increased risk of haematological malignancies in asplenic patients. The high baseline trafficking of T lymphocytes to splenic tissue suggests splenectomy may lead to loss of blood-borne malignant immunosurveillance that is not compensated for by the remaining SLT. To date, no quantitative analysis of the impact of splenectomy on the human T cell trafficking dynamics and tissue localisation has been reported. We developed a quantitative computational model that describes organ distribution and trafficking of human lymphocytes to explore the likely impact of splenectomy on immune cell distributions. In silico splenectomy resulted in an average reduction of T cell numbers in SLT by 35% (95%CI 0.12-0.97) and a comparatively lower, 9% (95%CI 0.17-1.43), mean decrease of T cell concentration in SLT. These results suggest that the surveillance capacity of the remaining SLT insufficiently compensates for the absence of the spleen. This may, in part, explain haematological malignancy risk in asplenic patients and raises the question of whether splenectomy has a clinically meaningful impact on patient responses to immunotherapy.

Two distinct clinical patterns of checkpoint inhibitor-induced thyroid dysfunction.

INTRODUCTION: Immune checkpoint inhibitors can lead to thyroid dysfunction. However, the understanding of the clinical phenotype of ICI-induced thyroid dysfunction in the real-world population is limited. The purpose of this study was to characterise the clinical patterns of dysfunction and evaluate the demographic, biochemical and immunological features associated with this patient cohort. MATERIALS AND METHODS: To characterise the longitudinal clinical course of thyroid dysfunction in patients from a single, UK regional cancer centre, a retrospective review of patients was conducted. Inclusion criteria included all patients treated with antiPD-1 checkpoint inhibitors (ICI), either as monotherapy (pembrolizumab/nivolumab) or in combination with a CTLA-4 inhibitor (ipilimumab). Patterns of toxicity were evaluated together with assessment of antibody titres. RESULTS: Over 16 months, thyroid dysfunction was seen in 13/90 and 3/13 patients treated with anti-PD1 monotherapy and in combination with ipilimumab, respectively. Patients either developed hyperthyroidism followed by hypothyroidism (12/16) or de novo hypothyroidism (4/16). Most patients were female (n = 11). All patients required thyroid replacement therapy. There was no relationship between clinical pattern of dysfunction and the presence of thyroid autoantibodies. CONCLUSIONS: There are two distinct patterns of thyroid dysfunction in ICI-treated patients. Patients with thyroiditis develop subsequent hypothyroidism in the vast majority of cases. The potential benefit from steroids or other therapy to manage the hyperthyroid phase remains unclear. Early detection of these patients through appropriate monitoring will improve clinical management and early hormone replacement, reducing the symptomatic burden of hypothyroidism.

Combining Mathematical Models With Experimentation to Drive Novel Mechanistic Insights Into Macrophage Function.

This perspective outlines an approach to improve mechanistic understanding of macrophages in inflammation and tissue homeostasis, with a focus on human inflammatory bowel disease (IBD). The approach integrates wet-lab and in-silico experimentation, driven by mechanistic mathematical models of relevant biological processes. Although wet-lab experimentation with genetically modified mouse models and primary human cells and tissues have provided important insights, the role of macrophages in human IBD remains poorly understood. Key open questions include: (1) To what degree hyperinflammatory processes (e.g., gain of cytokine production) and immunodeficiency (e.g., loss of bacterial killing) intersect to drive IBD pathophysiology? and (2) What are the roles of macrophage heterogeneity in IBD onset and progression? Mathematical modeling offers a synergistic approach that can be used to address such questions. Mechanistic models are useful for informing wet-lab experimental designs and provide a knowledge constrained framework for quantitative analysis and interpretation of resulting experimental data. The majority of published mathematical models of macrophage function are based either on animal models, or immortalized human cell lines. These experimental models do not recapitulate important features of human gastrointestinal pathophysiology, and, therefore are limited in the extent to which they can fully inform understanding of human IBD. Thus, we envision a future where mechanistic mathematical models are based on features relevant to human disease and parametrized by richer human datasets, including biopsy tissues taken from IBD patients, human organ-on-a-chip systems and other high-throughput clinical data derived from experimental medicine studies and/or clinical trials on IBD patients.

Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level.

Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (M&S) has been eagerly anticipated, and in this perspective, we highlight examples in which ML and M&S approaches can be integrated as complementary parts of a clinical pharmacology workflow.

An in silico model of cytotoxic T-lymphocyte activation in the lymph node following short peptide vaccination.

Tumour immunotherapy is dependent upon activation and expansion of tumour-targetting immune cells, known as cytotoxic T-lymphocytes (CTLs). Cancer vaccines developed in the past have had limited success and the mechanisms resulting in failure are not well characterized. To elucidate these mechanisms, we developed a human-parametrized, in silico, agent-based model of vaccination-driven CTL activation within a clinical short-peptide vaccination context. The simulations predict a sharp transition in the probability of CTL activation, which occurs with variation in the separation rate (or off-rate) of tumour-specific immune response-inducing peptides (cognate antigen) from the major histocompatibility class I (MHC-I) receptors of dendritic cells (DCs) originally at the vaccination site. For peptides with MHC-I off-rates beyond this transition, it is predicted that no vaccination strategy will lead to successful expansion of CTLs. For slower off-rates, below the transition, the probability of CTL activation becomes sensitive to the numbers of DCs and T cells that interact subsequent to DC migration to the draining lymph node of the vaccination site. Thus, the off-rate is a key determinant of vaccine design.

Computational prediction of human metabolic pathways from the complete human genome.

BACKGROUND: We present a computational pathway analysis of the human genome that assigns enzymes encoded therein to predicted metabolic pathways. Pathway assignments place genes in their larger biological context, and are a necessary first step toward quantitative modeling of metabolism. RESULTS: Our analysis assigns 2,709 human enzymes to 896 bioreactions; 622 of the enzymes are assigned roles in 135 predicted metabolic pathways. The predicted pathways closely match the known nutritional requirements of humans. This analysis identifies probable omissions in the human genome annotation in the form of 203 pathway holes (missing enzymes within the predicted pathways). We have identified putative genes to fill 25 of these holes. The predicted human metabolic map is described by a Pathway/Genome Database called HumanCyc, which is available at http://HumanCyc.org/. We describe the generation of HumanCyc, and present an analysis of the human metabolic map. For example, we compare the predicted human metabolic pathway complement to the pathways of Escherichia coli and Arabidopsis thaliana and identify 35 pathways that are shared among all three organisms. CONCLUSIONS: Our analysis elucidates a significant portion of the human metabolic map, and also indicates probable unidentified genes in the genome. HumanCyc provides a genome-based view of human nutrition that associates the essential dietary requirements of humans with a set of metabolic pathways whose existence is supported by the human genome. The database places many human genes in a pathway context, thereby facilitating analysis of gene expression, proteomics, and metabolomics datasets through a publicly available online tool called the Omics Viewer.