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The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB‐activated pathways we have constructed, trained and analyzed a mass action model of immediate‐early signaling involving ErbB1–4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log‐linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K d for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch‐like responses. Thus, key system‐wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation. Synopsis The four transmembrane receptors of the ErbB family, of which the epidermal growth factor tyrosine kinase (EGFR or ErbB1) is the founding member, are widely expressed in human tissues and stimulate diverse cellular responses, such as proliferation, survival and motility. ErbB receptors have been studied extensively at a molecular level and several have been shown to play a central role in human cancer. ErbB2 (Her2), for example, is overexpressed in a subset of breast cancers and is the target of trastuzumab, an important anticancer drug. Signaling through ErbB receptors, and the downstream proteins they activate, is complex because ErbB1–4 combine to form both homo‐ and heterodimers having distinct affinities for 13 known ligands and intracellular adaptor proteins. Understanding and predicting how signaling varies with receptor mutation or overexpression is essentially impossible using informal reasoning and pictorial representations of signaling pathways. To address this challenge, we have constructed, trained and analyzed a mass action model of immediate‐early signaling involving four ErbB receptors, as well as the downstream MAP kinase and PI3K–Akt signaling pathways involved in regulating cell proliferation (Figure 1 ). Our model aims to capture as much mechanistic information as possible, based on an extensive literature, while accurately representing the responses of cells to two major ligand subclasses (EGF and heregulin in multiple tumor cell types). In assembling such a model, we encounter a number of challenges, among which the processes of entraining models to experimental data are the most serious. Mass action models are governed by two kinds of parameters: the concentrations of individual protein species, and the rate constants governing protein–protein association and enzyme catalysis. Models of cell signaling that are realistic at a molecular level may inevitably contain many parameters that are unknown a priori . These parameters can be estimated either by direct measurement (usually in vitro , raising the question how values obtained in dilute solution should be translated to a crowded intracellular environment) or through an inverse process in which the model is fitted to data. The processes of training a kinetic model on experimental data constrain only a subset of parameters. It is assumed by some that the presence of parametric uncertainty in trained models makes them useless; in fact the aim of fitting is to determine only the subset of parameters that determine model performance with sufficient accuracy from which meaningful hypotheses can be drawn. Methods to accomplish this with cell signaling models are in their infancy, and most biochemical models are parameterized based on generic or theoretical assumptions; systematic or rigorous analysis of parameters variation and uncertainty has been restricted to small idealized models. In the current study, we use iterative fitting to generate families of model fits with similar biochemical connectivity, but different values for unconstrained parameters. We then attempt to draw well‐substantiated inferences from the families of models. To ascertain which features of our ErbB model are conserved when parameterized by different sets of rate constants and concentrations, we calculated dynamic sensitivities of multiple observables; this serves to measure the ‘importance’ of each rate or species in affecting measurable outputs. Sensitivity analysis showed that despite degeneracy in parameter values among families of fits, many important features are conserved. However, the rank order of sensitive parameters is strongly influenced by the feature being examined. For example, the proteins and rate constants that influence ERK activation differed from those that influence Akt, and factors important at high ligand concentrations differ from those important at low concentrations. A picture of ErbB signaling emerges in which different physiological outputs can be mapped back to specific biochemical characteristics of signaling proteins under varying input conditions. One striking aspect of this input–output behavior involves the relationship between ligand concentration (the input) and the activities of ERK or Akt (the output). A priori , we might assume little activation of the ErbB pathway when the concentration of ligand is below receptor K d . However, experiments demonstrate significant Akt and ERK activation (∼20% maximal) and 100‐fold lower ligand. Moreover, as ligand levels rise we would expect the response to rise ∼9‐fold for every 81‐fold increase in ligand concentrations (representing standard binding thermodynamics). In contrast, we observe a log‐linear amplification relationship between ligand and output over a nearly 10 6 concentration range (Figure 7 ). To understand how this might arise, we have examined the performance of our parameterized ErbB model of and subsets of model comprising coupled reactions such as the MAP kinase cascade. We conclude that the unexpected log‐linear input–output behavior of the ErbB model arises from interactions between enzyme cascades that in isolation exhibit canonical Hill‐like behavior. Thus, we must now think carefully about the ways in which signaling modules interact so as to generate behavior that is quite different from what we would expect of the isolated molecules or small cascades. In conclusion, we describe an approach in constructing and analyzing models of cell signaling pathways that can incorporate substantial molecular detail, while remaining sensitive to the inability of experiments to fully constrain parametric complexity. By focusing on well‐substantiated model features, we attempt to understand the origins of an unexpected and physiologically significant characteristic of the ErbB network with regard to dose–response behavior. Looking forward we anticipate building on the current model by including more accurate representations of more reactions, while developing a rigorous approach to generating model‐derived hypotheses, the degree of belief of which is determined by the underlying uncertainty in the model and the data. We will then be in a position to reliably understand cellular physiology in terms of molecular mechanism. A complex mass‐action model of immediate‐early ErbB signaling in human cells yields new biochemical insight despite its non‐identifiability and resulting parametric uncertainty Sensitivity analysis reveals a strong relationship between those parameters that are important for model dynamics and the specific physiological feature being analyzed: biochemical values that are highly significant for some responses are unimportant for others Proteins downstream in the ErbB signaling cascade are activated to substantial levels at ligand concentrations several orders of magnitude below those that result in appreciable receptor‐ligand binding or receptor phosphorylation The extreme sensitivity of ErbB receptors to low ligand concentrations arises from the ability of MAPK and PI3K‐Akt kinase cascades to amplify signals in a highly non‐linear manner; this behavior is lost when the cascades are isolated from the larger ErbB network, demonstrating context sensitivity in their operation

Background Dual sensory impairment (DSI), defined as concomitant hearing and vision loss, is a rare but clinically significant condition. While genetic factors account for the majority of congenital sensory impairments, dual impairment syndromes are less frequently documented. Case presentation This case report presents two siblings diagnosed with autosomal recessive high myopia and sensorineural hearing loss due to a homozygous mutation in the SLITRK6 gene. Conclusion Our findings reinforce the essential role of the SLITRK6 gene in auditory and visual system development and highlight the importance of genetic evaluation in pediatric patients presenting with multi-sensory deficits.

Understanding the therapeutic effect of drug dose and scheduling is critical to inform the design and implementation of clinical trials. The increasing complexity of both mono and particularly combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Using a systems pharmacology approach, we have extended an existing PK-PD model of tumor growth with a mechanistic model of the cell cycle, enabling simulation of mono and combination treatment with the ATR inhibitor AZD6738 and ionizing radiation. Using AZD6738, we have developed multi-parametric cell based assays measuring DNA damage and cell cycle transition, providing quantitative data suitable for model calibration. Our in vitro calibrated cell cycle model is predictive of tumor growth observed in in vivo mouse xenograft studies. The model is being used for phase I clinical trial designs for AZD6738, with the aim of improving patient care through quantitative dose and scheduling prediction.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model was used for preclinical to clinical translation of inotuzumab ozogamicin, a CD22-targeting antibody-drug conjugate (ADC) for B cell malignancies including non-Hodgkin’s lymphoma (NHL) and acute lymphocytic leukemia (ALL). Preclinical data was integrated in a PK/PD model which included (1) a plasma PK model characterizing disposition and clearance of inotuzumab ozogamicin and its released payload N-Ac-γ-calicheamicin DMH, (2) a tumor disposition model describing ADC diffusion into the tumor extracellular environment, (3) a cellular model describing inotuzumab ozogamicin binding to CD22, internalization, intracellular N-Ac-γ-calicheamicin DMH release, binding to DNA, or efflux from the tumor cell, and (4) tumor growth and inhibition in mouse xenograft models. The preclinical model was translated to the clinic by incorporating human PK for inotuzumab ozogamicin and clinically relevant tumor volumes, tumor growth rates, and values for CD22 expression in the relevant patient populations. The resulting stochastic models predicted progression-free survival (PFS) rates for inotuzumab ozogamicin in patients comparable to the observed clinical results. The model suggested that a fractionated dosing regimen is superior to a conventional dosing regimen for ALL but not for NHL. Simulations indicated that tumor growth is a highly sensitive parameter and predictive of successful outcome. Inotuzumab ozogamicin PK and N-Ac-γ-calicheamicin DMH efflux are also sensitive parameters and would be considered more useful predictors of outcome than CD22 receptor expression. In summary, a multiscale, mechanism-based model has been developed for inotuzumab ozogamicin, which can integrate preclinical biomeasures and PK/PD data to predict clinical response.

Monoclonal antibody (mAb) pharmacokinetics (PK) have largely been predicted via allometric scaling with little consideration for cross‐species differences in neonatal Fc receptor (FcRn) affinity or clearance/distribution mechanisms. To address this, we developed a mAb physiologically‐based PK model that describes the intracellular trafficking and FcRn recycling of mAbs in a human FcRn transgenic homozygous mouse and human. This model uses mAb‐specific in vitro data together with species‐specific FcRn tissue expression, tissue volume, and blood‐flow physiology to predict mAb in vivo linear PK a priori. The model accurately predicts the terminal half‐life of 90% of the mAbs investigated within a twofold error. The mechanistic nature of this model allows us to not only predict linear PK from in vitro data but also explore the PK and target binding of mAbs engineered to have pH‐dependent binding to its target or FcRn and could aid in the selection of mAbs with optimal PK and pharmacodynamic properties.

Lamins are essential components of the nuclear envelope and have been studied for decades due to their involvement in several devastating human diseases, the laminopathies. Despite intensive research, the molecular basis behind the disease state remains mostly unclear with a number of conflicting results regarding the different cellular functions of nuclear lamins being published. The field of developmental biology is no exception. Across model organisms, the types of lamins present in early mammalian development have been contradictory over the years. Due to the long half-life of the lamin proteins, which is a maternal factor that gets carried over to the zygote after fertilization, investigators are posed with challenges to dive into the functional aspects and significance of lamins in development. Due to these technical limitations, the role of lamins in early mammalian embryos is virtually unexplored. This review aims in converging results that were obtained so far in addition to the complex functions that ceases if lamins are mutated.

Background Ototoxicity caused by aminoglycosides like kanamycin and amikacin used in the treatment of MDR TB has devastating effects on their life. These negative impacts can be reduced with proper aural rehabilitation. Cochlear implant is a part of aural rehabilitation which is found effective in cases who are not benefited with hearing aids; hence, cochlear implant’s long-term effectiveness in its users are important to be identified and documented. Case presentation Three young adults with MDR TB who developed hearing loss after treating with kanamycin/amikacin are considered. All of them were getting poor benefit with hearing aids and underwent cochlear implantation. Responses with cochlear implant were excellent and its long-term effect also showed reduction in the disabilities and handicap 5 years post-implantation. Conclusion Cochlear implantation is successful in MDR TB cases with kanamycin/amikacin-induced ototoxicity.