In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever‐increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Tissue-resident macrophages are key players in inflammatory processes, and their activation and functionality are crucial in health and disease. Numerous diseases are associated with alterations in homeostasis or dysregulation of the innate immune system, including allergic reactions, autoimmune diseases, and cancer. Macrophages are a prime target for drug discovery due to their major regulatory role in health and disease. Currently, the main sources of macrophages used for therapeutic compound screening are primary cells isolated from blood or tissue or immortalized or neoplastic cell lines (e.g., THP-1). Here, we describe an improved method to employ induced pluripotent stem cells (iPSCs) for the high-yield, large-scale production of cells resembling tissue-resident macrophages. For this, iPSC-derived macrophage-like cells are thoroughly characterized to confirm their cell identity and thus their suitability for drug screening purposes. These iPSC-derived macrophages show strong cellular identity with primary macrophages and recapitulate key functional characteristics, including cytokine release, phagocytosis, and chemotaxis. Furthermore, we demonstrate that genetic modifications can be readily introduced at the macrophage-like progenitor stage in order to interrogate drug target-relevant pathways. In summary, this novel method overcomes previous shortcomings with primary and leukemic cells and facilitates large-scale production of genetically modified iPSC-derived macrophages for drug screening applications.

Gene signatures of drug-induced toxicity are of broad interest, but they are often identified from small-scale, single-time point experiments, and are therefore of limited applicability. To address this issue, we performed multivariate analysis of gene expression, cell-based assays, and histopathological data in the TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system) database. Data mining highlights four genes—EGR1, ATF3, GDF15 and FGF21—that are induced 2 h after drug administration in human and rat primary hepatocytes poised to eventually undergo cytotoxicity-induced cell death. Modelling and simulation reveals that these early stress-response genes form a functional network with evolutionarily conserved structure and intrinsic dynamics. This is underlined by the fact that early induction of this network in vivo predicts drug-induced liver and kidney pathology with high accuracy. Our findings demonstrate the value of early gene-expression signatures in predicting and understanding compound-induced toxicity. The identified network can empower first-line tests that reduce animal use and costs of safety evaluation.

Chronic hepatitis B virus (HBV) infection affects 300 million patients worldwide^1,2, in whom virus-specific CD8 T cells by still ill-defined mechanisms lose their function and cannot eliminate HBV-infected hepatocytes^3–7. Here we demonstrate that a liver immune rheostat renders virus-specific CD8 T cells refractory to activation and leads to their loss of effector functions. In preclinical models of persistent infection with hepatotropic viruses such as HBV, dysfunctional virus-specific CXCR6⁺ CD8 T cells accumulated in the liver and, as a characteristic hallmark, showed enhanced transcriptional activity of cAMP-responsive element modulator (CREM) distinct from T cell exhaustion. In patients with chronic hepatitis B, circulating and intrahepatic HBV-specific CXCR6⁺ CD8 T cells with enhanced CREM expression and transcriptional activity were detected at a frequency of 12–22% of HBV-specific CD8 T cells. Knocking out the inhibitory CREM/ICER isoform in T cells, however, failed to rescue T cell immunity. This indicates that CREM activity was a consequence, rather than the cause, of loss in T cell function, further supported by the observation of enhanced phosphorylation of protein kinase A (PKA) which is upstream of CREM. Indeed, we found that enhanced cAMP–PKA-signalling from increased T cell adenylyl cyclase activity augmented CREM activity and curbed T cell activation and effector function in persistent hepatic infection. Mechanistically, CD8 T cells recognizing their antigen on hepatocytes established close and extensive contact with liver sinusoidal endothelial cells, thereby enhancing adenylyl cyclase–cAMP–PKA signalling in T cells. In these hepatic CD8 T cells, which recognize their antigen on hepatocytes, phosphorylation of key signalling kinases of the T cell receptor signalling pathway was impaired, which rendered them refractory to activation. Thus, close contact with liver sinusoidal endothelial cells curbs the activation and effector function of HBV-specific CD8 T cells that target hepatocytes expressing viral antigens by means of the adenylyl cyclase–cAMP–PKA axis in an immune rheostat-like fashion.

Background: The NLRP3 inflammasome drives release of pro‐inflammatory cytokines including interleukin (IL)‐1β and IL‐18 and is a potential target for ulcerative colitis (UC). Selnoflast (RO7486967) is an orally active, potent, selective and reversible small molecule NLRP3 inhibitor. We conducted a randomized, placebo‐controlled Phase 1b study to assess the safety, tolerability, pharmacokinetics (PK) and pharmacodynamics (PD) of selnoflast.Methods: Nineteen adults with previous diagnosis of UC and current active moderate to severe disease were randomized 2:1 to selnoflast or placebo for 7 days. A dose of 450 mg QD (once daily) was selected to achieve 90% IL‐1β inhibition in plasma and colon tissue. Consecutive blood, sigmoid colon biopsies and stool samples were analyzed for a variety of PD markers. Safety and PK were also evaluated.Results: Selnoflast was well‐tolerated. Plasma concentrations increased rapidly after oral administration, reaching T_max 1 h post‐dose. Mean plasma concentrations stayed above the IL‐1β IC₉₀ level throughout the dosing interval (mean C_trough on Day 1 and Day 5: 2.55 μg/mL and 2.66 μg/mL, respectively). At steady state, post‐dose selnoflast concentrations in sigmoid colon (5‐20 μg/g) were above the IC₉₀. Production of IL‐1β was reduced in whole blood following ex vivo stimulation with lipopolysaccharide (LPS) (in the selnoflast arm). No changes were observed in plasma IL‐18 levels. There were no meaningful differences in the expression of an IL‐1‐related gene signature in sigmoid colon tissue, and no differences in the expression of stool biomarkers.Conclusions: Selnoflast was safe and well‐tolerated. Selnoflast 450 mg QD achieved plasma and tissue exposure predicted to maintain IL‐1β IC₉₀ over the dosing interval. However, PD biomarker results showed no robust differences between treatment arms, suggesting no major therapeutic effects are to be expected in UC. The limitations of this study are its small sample size and indirect assessment of the effect on IL‐1β in tissue.Trial registration: ISRCTN16847938