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The Adverse Outcome Pathway (AOP) biological framework was introduced in 2012, yet defining a mathematical/computational framework for quantitative AOP (qAOP) development remains an open problem. In order to properly unravel the intricate biological mechanisms described by AOPs and provide quantitative predictions to support risk assessment, a computational model should provide a clear time-course prediction of key events (KEs), as well as describe the key event relationships (KERs) linking a molecular initiating event (MIE) to an adverse outcome (AO). Ultimately, the mathematical description of those links entails the possibility of quantitatively predicting adverse effects based on early events.

•Three approaches to AOP quantification are demonstrated and discussed.•Dose-response based qAOPs are easy to develop but have limited explanatory power.•Bayesian networks qAOPs are more flexible but more difficult to calibrate.•A systems biology approach can be complex but offers deeper understanding.•A combination of the above approaches is recommended.

Emerging data show that tissue-resident memory T (TRM) cells play an important protective role at murine and human barrier sites. TRMcells in the epidermis of mouse skin patrol their surroundings and rapidly respond when antigens are encountered. However, whether a similar migratory behavior is performed by human TRMcells is unclear, as technology to longitudinally follow them in situ has been lacking. To address this issue, we developed an ex vivo culture system to label and track T cells in fresh skin samples. We validated this system by comparing in vivo and ex vivo properties of murine TRMcells. Using nanobody labeling, we subsequently demonstrated in human ex vivo skin that CD8+TRMcells migrated through the papillary dermis and the epidermis, below sessile Langerhans cells. Collectively, this work allows the dynamic study of resident immune cells in human skin and provides evidence of tissue patrol by human CD8+TRMcells.

CAR T cells represent a potentially curative strategy for B cell malignancies. However, the outcome and dynamics of CAR T cell interactions in distinct anatomical sites are poorly understood. Using intravital imaging, we tracked interactions established by anti-CD19 CAR T cells in B cell lymphoma–bearing mice. Circulating targets trapped CAR T cells in the lungs, reducing their access to lymphoid organs. In the bone marrow, tumor apoptosis was largely due to CAR T cells that engaged, killed, and detached from their targets within 25 min. Notably, not all CAR T cell contacts elicited calcium signaling or killing while interacting with tumors, uncovering extensive functional heterogeneity. Mathematical modeling revealed that direct killing was sufficient for tumor regression. Finally, antigen-loss variants emerged in the bone marrow, but not in lymph nodes, where CAR T cell cytotoxic activity was reduced. Our results identify a previously unappreciated level of diversity in the outcomes of CAR T cell interactions in vivo, with important clinical implications.

Cytotoxic T lymphocyte (CTL)-mediated killing involves the formation of a synapse with a target cell, followed by delivery of perforin and granzymes. Previously, we derived a general functional response for CTL killing while considering that CTLs form stable synapses (i.e., single-stage) and that the number of conjugates remains at steady state. However, the killing of target cells sometimes requires multiple engagements (i.e., multistage). To study how multistage killing and a lack of steady state influence the functional response, we here analyze a set of differential equations as well as simulations employing the cellular Potts model, in both cases describing CTLs that kill target cells. We find that at steady state the total killing rate (i.e., the number of target cells killed by all CTLs) is well described by the previously derived double saturation function. Compared to single-stage killing, the total killing rate during multistage killing saturates at higher CTL and target cell densities. Importantly, when the killing is measured before the steady state is approached, a qualitatively different functional response emerges for two reasons: First, the killing signal of each CTL gets diluted over several targets and because this dilution effect is strongest at high target cell densities; this can result in a peak in the dependence of the total killing rate on the target cell density. Second, the total killing rate exhibits a sigmoid dependence on the CTL density when killing is a multistage process, because it takes typically more than one CTL to kill a target. In conclusion, a sigmoid dependence of the killing rate on the CTLs during initial phases of killing may be indicative of a multistage killing process. Observation of a sigmoid functional response may thus arise from a dilution effect and is not necessarily due to cooperative behavior of the CTLs.

ABSTRACTIntroduction: Drug-induced liver injury (DILI) is a significant threat to human health and a major problem in drug development. It is hard to predict due to its idiosyncratic nature and which does not show up in animal trials. Hepatic adaptive stress response pathway activation is generally observed in drug-induced liver injury. Dynamical pathway modeling has the potential to foresee adverse effects of drugs before they go in trial. Ordinary differential equation modeling can offer mechanistic insight, and allows us to study the dynamical behavior of stress pathways involved in DILI.Areas covered: This review provides an overview on the progress of the dynamical modeling of stress and death pathways pertinent to DILI, i.e. pathways relevant for oxidative stress, inflammatory stress, DNA damage, unfolded proteins, heat shock and apoptosis. We also discuss the required steps for applying such modeling to the liver.Expert opinion: Despite the strong progress made since the turn of the century, models of stress pathways have only rarely been specifically applied to describe pathway dynamics for DILI. We argue that with minor changes, in some cases only to parameter values, many of these models can be repurposed for application in DILI research. Combining both dynamical models with in vitrotesting might offer novel screening methods for the harmful side-effects of drugs.

Cytotoxic T lymphocytes (CTLs) kill virus-infected cells and tumor cells, and play a critical role in immune protection. Our knowledge of how the CTL killing efficiency varies with CTL and target cell numbers is limited. Here, we simulate a region of lymphoid tissue using a cellular Potts model to characterize the functional response of CTL killing of target cells, and find that the total killing rate saturates both with the CTL and the target cell densities. The relative saturation in CTL and target cell densities is determined by whether a CTL can kill multiple target cells at the same time, and whether a target cell can be killed by many CTLs together. We find that all the studied regimes can be well described by a double-saturation (DS) function with two different saturation constants. We show that this DS model can be mechanistically derived for the cases where target cells are killed by a single CTL. For the other cases, a biological interpretation of the parameters is still possible. Our results imply that this DS function can be used as a tool to predict the cellular interactions in cytotoxicity data.

There is strong evidence that both adoptive T cell transfer and T cell checkpoint blockade can lead to regression of human melanoma. However, little data are available on the effect of these cancer therapies on the tumor-reactive T cell compartment. To address this issue we have profiled therapy-induced T cell reactivity against a panel of 145 melanoma-associated CD8+T cell epitopes. Using this approach, we demonstrate that individual tumor-infiltrating lymphocyte cell products from melanoma patients contain unique patterns of reactivity against shared melanoma-associated antigens, and that the combined magnitude of these responses is surprisingly low. Importantly, TIL therapy increases the breadth of the tumor-reactive T cell compartment in vivo, and T cell reactivity observed post-therapy can almost in full be explained by the reactivity observed within the matched cell product. These results establish the value of high-throughput monitoring for the analysis of immuno-active therapeutics and suggest that the clinical efficacy of TIL therapy can be enhanced by the preparation of more defined tumor-reactive T cell products.

An increasing body of evidence emphasizes the role of tissue-resident memory T cells (TRM) in the defense against recurring pathogens and malignant neoplasms. However, little is known with regard to the origin of these cells and their kinship to other CD8+ T cell compartments. To address this issue, we followed the antigen-specific progeny of individual naive CD8+ T cells to the T effector (TEFF), T circulating memory (TCIRCM), and TRM pools by lineage-tracing and single-cell transcriptome analysis. We demonstrate that a subset of T cell clones possesses a heightened capacity to form TRM, and that enriched expression of TRM–fate-associated genes is already apparent in the circulating TEFF offspring of such clones. In addition, we demonstrate that the capacity to generate TRM is permanently imprinted at the clonal level, before skin entry. Collectively, these data provide compelling evidence for early stage TRM fate decisions and the existence of committed TRM precursor cells in the circulatory TEFF compartment.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.