A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema.

Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20 pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300 mg every 2 weeks (Q2W) to every 4 weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.

Global survey on the surgical management of patients affected by colorectal cancer with synchronous liver metastases: impact of surgical specialty and geographic region.

BACKGROUND: Consensus on the best surgical strategy for the management of synchronous colorectal liver metastases (sCRLM) has not been achieved. This study aimed to assess the attitudes of surgeons involved in the treatment of sCRLM. METHODS: Surveys designed for colorectal, hepato-pancreato-biliary (HPB), and general surgeons were disseminated through representative societies. Subgroup analyses were performed to compare responses between specialties and continents. RESULTS: Overall, 270 surgeons (57 colorectal, 100 HPB and 113 general surgeons) responded. Specialist surgeons more frequently utilized minimally invasive surgery (MIS) than general surgeons for colon (94.8% vs. 71.7%, p < 0.001), rectal (91.2% vs. 64.6%, p < 0.001), and liver resections (53% vs. 34.5%, p = 0.005). In patients with an asymptomatic primary, the liver-first two-stage approach was preferred in most respondents' centres (59.3%), while the colorectal-first approach was preferred in Oceania (83.3%) and Asia (63.4%). A substantial proportion of the respondents (72.6%) had personal experience with minimally invasive simultaneous resections, and an expanding role for this procedure was foreseen (92.6%), while more evidence was desired (89.6%). Respondents were more reluctant to combine a hepatectomy with low anterior (76.3%) and abdominoperineal resections (73.3%), compared to right (94.4%) and left hemicolectomies (90.7%). Colorectal surgeons were less inclined to combine right or left hemicolectomies with a major hepatectomy than HPB and general surgeons (right: 22.8% vs. 50% and 44.2%, p = 0.008; left: 14% vs. 34% and 35.4%, p = 0.002, respectively). CONCLUSION: The clinical practices and viewpoints on the management of sCRLM differ between continents, and between and within surgical specialties. However, there appears to be consensus on a growing role for MIS and a need for evidence-based input.

Mapping the prevalence of severe acute malnutrition in Papua, Indonesia by using geostatistical models.

BACKGROUND: Severe acute malnutrition (SAM) is the most life-threatening form of malnutrition, and in 2019, approximately 14.3 million children under the age of 5 were considered to have SAM. The prevalence of child malnutrition is recorded through large-scale household surveys run at multi-year intervals. However, these surveys are expensive, yield estimates with high levels of aggregation, are run over large time intervals, and may show gaps in area coverage. Geospatial modelling approaches could address some of these challenges by combining geo-located survey data with geospatial data to produce mapped estimates that predict malnutrition risk in both surveyed and non-surveyed areas. METHODS: A secondary analysis of cluster-level program evaluation data (n = 123 primary sampling units) was performed to map severe acute malnutrition (SAM) in Papuan children under 2 years (0-23 months) of age with a spatial resolution of 1 × 1 km in Papua, Indonesia. The approach used Bayesian geostatistical modelling techniques and publicly available geospatial data layers. RESULTS: In Papua, Indonesia, SAM was predicted in geostatistical models by using six geospatial covariates related primarily to conditions of remoteness and inaccessibility. The predicted 1-km spatial resolution maps of SAM showed substantial spatial variation across the province. By combining the predicted rates of SAM with estimates of the population under 2 years of age, the prevalence of SAM in late 2018 was estimated to be around 15,000 children (95% CI 10,209-26,252). Further tests of the predicted levels suggested that in most areas of Papua, more than 5% of Papuan children under 2 years of age had SAM, while three districts likely had more than 15% of children with SAM. CONCLUSIONS: Eradication of hunger and malnutrition remains a key development goal, and more spatially detailed data can guide efficient intervention strategies. The application of additional household survey datasets in geostatistical models is one way to improve the monitoring and timely estimation of populations at risk of malnutrition. Importantly, geospatial mapping can yield insights for both surveyed and non-surveyed areas and can be applied in low-income country contexts where data is scarce and data collection is expensive or regions are inaccessible.

Incidence of severe acute malnutrition after treatment: A prospective matched cohort study in Sokoto, Nigeria.

Severe acute malnutrition (SAM) among children in Nigeria is tackled through the outpatient therapeutic programme (OTP) of the Community-based Management of Acute Malnutrition (CMAM) programme. CMAM is evidently effective in resolving SAM, but little evidence exists on the remaining risk of SAM relapse for children discharged as cured from the OTP. We aimed to measure and compare the 6-month incidence of SAM among OTP-cured and community control children and identify factors associated with SAM relapse. We conducted a prospective matched cohort study that tracked 553 OTP-cured and 526 control children in Sokoto State, Northern Nigeria. Outcomes and covariates were measured fortnightly in up to 12 home visits. We used multivariate Cox and accelerated failure time models to identify significant risk correlates, where the covariates to be tested for correlation with relapse were selected using domain knowledge and automatic feature selection methods. SAM incidence rates were 52 times higher in the OTP-cured cohort (0.204/100 child-days) than in the community control cohort (0.004/100 child-days). Children with lower mid-upper arm circumference at OTP admission, with lower height/length-for-age z-scores, whose household head did not work over the full year, who lived in an area previously affected by environmental shocks, who were female and who had diarrhoea before the visit had a significantly higher relapse risk. Our study shows that OTP-cured children remain at a significantly excess risk of SAM. To improve long-term health outcomes of these children, programmes adopting a CMAM approach should strengthen follow-up care and be integrated with other preventive services.

Características Sociodemográficas e Risco de Transmissão da COVID-19 em Moçambique / Sociodemographic Characteristics and Transmission Risk of COVID-19 in Mozambique

Objectivo: Mapear o potencial risco de transmissão do novo coronavírus em Moçambique de modo a identificar os distritos cujas características sociodemográficas favorecem a propagação do vírus. Métodos: Usou-se a modelação espacial para determinar o risco relativo de propagação da COVID-19 num distrito em relação ao outro com base nos seguintes factores sociodemográficos: densidade populacional, tamanho médio de agregado familiar, percentagem da população jovem de 15-34 anos e percentagem da população que vive num raio de 2 km de uma estrada classificada. Primeiro, para cada factor foi estimado um risco relativo dividindo os distritos em quintis, e, em segundo lugar, os riscos individuais de cada factor foram somados com igual peso para estimar o risco agregado de transmissão da COVID-19 por distrito. Resultados: Dezanove distritos localizados sobretudo nos principais centros urbanos e no corredor da Beira apresentam alto risco de propagação da COVID-19 em função das suas características sociodemográficas; 24 distritos mostram risco médio-alto e distribuem-se pelas regiões centro e sul do país; 60 distritos localizados nas regiões centro e sul e no interior da região norte apresentam risco médio e; 58 distritos mostram risco médio-baixo ou baixo de transmissão da COVID-19 e encontram-se no litoral centro-norte do país. Conclusão: Os distritos cujo perfil sociodemográfico é favorável à rápida propagação do novo coronavírus são os das grandes cidades e os localizados nas principais rotas de transporte. No entanto, este padrão de risco é susceptível de alterações em função da celeridade, abrangência e níveis de observância das medidas de prevenção e/ou de mitigação da COVID-19. Assim, recomenda-se que as medidas de prevenção e mitigação tenham em conta o risco potencial em cada distrito em função das suas características sociodemográficas.

Objective: To map the potential risk of COVID-19 transmission in Mozambique in order to identify districts with sociodemographic characteristics that favour the spread of coronavirus. Methods: Spatial modelling was used to determine the relative risk of COVID-19 transmission in a certain district in relation to other districts based on the following sociodemographic factors: population density, mean number of household members, the percentage of the young population aged 15-34 and the proportion of a district's population living within two kilometres of a classified road. First, a relative risk due to each factor was estimated grouping the districts into quintiles and, second, the individual risks were added with equal weight to estimate the aggregate relative risk of COVID-19 transmission per district. Results: Nineteen districts located in the main urban centres and along the Beira corridor were found to be at a high relative risk of COVID-19 transmission; 24 districts located mainly in central and southern regions display a medium-high risk category; 60 districts located in the central and southern regions and in the hinterland of the northern region show a medium risk category and; 58 districts exhibit a medium-low or low risk category of COVID-19 transmission and are mainly located at the eastern part of the central-north region. Conclusion: The districts with sociodemographic profile favouring the spread of the new coronavirus are those in the big cities and those located along the main transportation routes. However, the pattern of risk is subject to changes due to the speed, coverage and level of compliance with COVID-19 prevention and mitigation measures. It is recommended that COVID-19 prevention and mitigation measures should take into account the potential risk of each district.

Use of translational modeling and simulation for quantitative comparison of PF-06804103, a new generation HER2 ADC, with Trastuzumab-DM1.

A modeling and simulation approach was used for quantitative comparison of a new generation HER2 antibody drug conjugate (ADC, PF-06804103) with trastuzumab-DM1 (T-DM1). To compare preclinical efficacy, the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of PF-06804103 and T-DM1 was determined across a range of mouse tumor xenograft models, using a tumor growth inhibition model. The tumor static concentration was assigned as the minimal efficacious concentration. PF-06804103 was concluded to be more potent than T-DM1 across cell lines studied. TSCs ranged from 1.0 to 9.8 µg/mL (n = 7) for PF-06804103 and from 4.7 to 29 µg/mL (n = 5) for T-DM1. Two experimental models which were resistant to T-DM1, responded to PF-06804103 treatment. A mechanism-based target mediated drug disposition (TMDD) model was used to predict the human PK of PF-06804103. This model was constructed and validated based on T-DM1 which has non-linear PK at doses administered in the clinic, driven by binding to shed HER2. Non-linear PK is predicted for PF-06804103 in the clinic and is dependent upon circulating HER2 extracellular domain (ECD) concentrations. The models were translated to human and suggested greater efficacy for PF-06804103 compared to T-DM1. In conclusion, a fit-for-purpose translational PK/PD strategy for ADCs is presented and used to compare a new generation HER2 ADC with T-DM1.

A Physiologically-Based Pharmacokinetic Model for the Prediction of "Half-Life Extension" and "Catch and Release" Monoclonal Antibody Pharmacokinetics.

Monoclonal antibodies (mAbs) can be engineered to have "extended half-life" and "catch and release" properties to improve target coverage. We have developed a mAb physiologically-based pharmacokinetic model that describes intracellular trafficking, neonatal Fc receptor (FcRn) recycling, and nonspecific clearance of mAbs. We extended this model to capture target binding as a function of target affinity, expression, and turnover. For mAbs engineered to have an extended half-life, the model was able to accurately predict the terminal half-life (82% within 2-fold error of the observed value) in the human FcRn transgenic (Tg32) homozygous mouse and human. The model also accurately captures the trend in pharmacokinetic and target coverage data for a set of mAbs with differing catch and release properties in the Tg32 mouse. The mechanistic nature of this model allows us to explore different engineering techniques early in drug discovery, potentially expanding the number of "druggable" targets.

A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat.

Gaucher's disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first-line SRT approved for GD1, on treatment-naïve or patients with ERT-stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype-phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.

Leveraging Quantitative Systems Pharmacology Approach into Development of Human Recombinant Follistatin Fusion Protein for Duchenne Muscular Dystrophy.

Quantitative understanding about the dynamics of drug-target interactions in biological systems is essential, especially in rare disease programs with small patient populations. Follistatin, by antagonism of myostatin and activin, which are negative regulators of skeletal muscle and inflammatory response, is a promising therapeutic target for Duchenne Muscular Dystrophy. In this study, we constructed a quantitative systems pharmacology model for FS-EEE-Fc, a follistatin recombinant protein to investigate its efficacy from dual target binding, and, subsequently, to project its human efficacious dose. Based on model simulations, with an assumed efficacy threshold of 7-10% muscle volume increase, 3-5 mg/kg weekly dosing of FS-EEE-Fc is predicted to achieve meaningful clinical outcome. In conclusion, the study demonstrated an application of mechanism driven approach at early stage of a rare disease drug development to support lead compound optimization, enable human dose, pharmacokinetics, and efficacy predictions.

A Physiologically-Based Pharmacokinetic Model for the Prediction of Monoclonal Antibody Pharmacokinetics From In Vitro Data.

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.

Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology.

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non-neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular-level, cellular-level, and organ-level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient-specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.

Systems Pharmacology Model of Gastrointestinal Damage Predicts Species Differences and Optimizes Clinical Dosing Schedules.

Gastrointestinal (GI) adverse events (AEs) are frequently dose limiting for oncology agents, requiring extensive clinical testing of alternative schedules to identify optimal dosing regimens. Here, we develop a translational mathematical model to predict these clinical AEs starting from preclinical GI toxicity data. The model structure incorporates known biology and includes stem cells, daughter cells, and enterocytes. Published data, including cellular numbers and division times, informed the system parameters for humans and rats. The drug-specific parameters were informed with preclinical histopathology data from rats treated with irinotecan. The model fit the rodent irinotecan-induced pathology changes well. The predicted time course of enterocyte loss in patients treated with weekly doses matched observed AE profiles. The model also correctly predicts a lower level of AEs for every 3 weeks (Q3W), as compared to the weekly schedule.

Preclinical to Clinical Translation of Antibody-Drug Conjugates Using PK/PD Modeling: a Retrospective Analysis of Inotuzumab Ozogamicin.

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.

Rapid Method To Determine Intracellular Drug Concentrations in Cellular Uptake Assays: Application to Metformin in Organic Cation Transporter 1-Transfected Human Embryonic Kidney 293 Cells.

Because of the importance of intracellular unbound drug concentrations in the prediction of in vivo concentrations that are determinants of drug efficacy and toxicity, a number of assays have been developed to assess in vitro unbound concentrations of drugs. Here we present a rapid method to determine the intracellular unbound drug concentrations in cultured cells, and we apply the method along with a mechanistic model to predict concentrations of metformin in subcellular compartments of stably transfected human embryonic kidney 293 (HEK293) cells. Intracellular space (ICS) was calculated by subtracting the [(3)H]-inulin distribution volume (extracellular space, ECS) from the [(14)C]-urea distribution volume (total water space, TWS). Values obtained for intracellular space (mean ± S.E.M.; µl/10(6) cells) of monolayers of HEK cells (HEK-empty vector [EV]) and cells overexpressing human organic cation transporter 1 (HEK-OCT1), 1.21± 0.07 and 1.25±0.06, respectively, were used to determine the intracellular metformin concentrations. After incubation of the cells with 5 µM metformin, the intracellular concentrations were 26.4 ± 7.8 µM and 268 ± 11.0 µM, respectively, in HEK-EV and HEK-OCT1. In addition, intracellular metformin concentrations were lower in high K(+) buffer (140 mM KCl) compared with normal K(+) buffer (5.4 mM KCl) in HEK-OCT1 cells (54.8 ± 3.8 µM and 198.1 ± 11.2 µM, respectively; P < 0.05). Our mechanistic model suggests that, depending on the credible range of assumed physiologic values, the positively charged metformin accumulates to particularly high levels in endoplasmic reticulum and/or mitochondria. This method together with the computational model can be used to determine intracellular unbound concentrations and to predict subcellular accumulation of drugs in other complex systems such as primary cells.

Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738.

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.

Comprehensive mechanism-based antibody pharmacokinetic modeling.

Pharmacokinetic models of antibody distribution and dynamics are useful for predicting and optimizing therapeutic behavior. Targeted antigens are produced and distributed in various tissues in specific patterns in disease phenotypes. Existing models leave out significant mechanistic detail which would enable an understanding of how to modify therapeutics in an optimal manner to allow appropriate tissue penetration in either a healthy or diseased state. The model presented here incorporates additional complexity such as diffusion through endothelial barriers, differential transcytosis properties, FcRn-mediated recycling, and incorporates these properties in an organ-specific manner. This creates a platform which can be expanded upon to include understanding of the effect of target on therapeutic distribution and clearance, differences in dynamics during a diseased versus healthy state, differential dose strategies, and mechanistic translation between animal models and human disease state. This model represents a superior alternative to typical and potentially over-simplified scaling strategies utilized in most existing physiologically-based pharmacokinetic models. Ultimately, this will enable better therapeutic design and greater pharmacological effects.

Impaired SHP2-mediated extracellular signal-regulated kinase activation contributes to gefitinib sensitivity of lung cancer cells with epidermal growth factor receptor-activating mutations.

Most non-small cell lung cancers (NSCLC) display elevated expression of epidermal growth factor receptor (EGFR), but response to EGFR kinase inhibitors is predominantly limited to NSCLC harboring EGFR-activating mutations. These mutations are associated with increased activity of survival pathways, including phosphatidylinositol 3-kinase/AKT and signal transducer and activator of transcription 3/5. We report that EGFR-activating mutations also surprisingly lead to decreased ability to activate extracellular signal-regulated kinase (ERK) compared with wild-type EGFR. In NSCLC cells and mouse embryonic fibroblasts expressing mutant EGFR, this effect on ERK correlates with decreased EGFR internalization and reduced phosphorylation of SHP2, a tyrosine phosphatase required for the full activation of ERK. We further show that ERK activation levels affect cellular response to gefitinib. NSCLC cells with EGFR mutation display reduced gefitinib sensitivity when ERK activation is augmented by expression of constitutively active mutants of mitogen-activated protein kinase/ERK kinase (MEK). Conversely, in a NSCLC cell line expressing wild-type EGFR, gefitinib treatment along with or following MEK inhibition increases death response compared with treatment with gefitinib alone. Our results show that EGFR-activating mutations may promote some survival pathways but simultaneously impair others. This multivariate alteration of the network governing cellular response to gefitinib, which we term "oncogene imbalance," portends a potentially broader ability to treat gefitinib-resistant NSCLC.

Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.

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.

B lymphocyte deficiency in IgH-transgenic rabbits.

We developed IgH-transgenic rabbits carrying a productive VDJ-Cmu Tg and found the rabbits were B cell-deficient, with a 50-100% reduction in serum IgM and IgG levels. The bone marrow of newborn Tg rabbits contained severely reduced levels of preB cells and almost no B cells. The few preB cells present in the bone marrow were large, cycling cells that expressed the VDJ-Cmu Tg, indicating that the block in B cell development likely occurred at or before the transition from large (early) preB to small (late) preB cells. By immunoprecipitation, the Tg mu-chain paired with VpreB and lambda5, suggesting that the B cell deficiency is not due to an inability to form a preB cell receptor. Despite the block in B cell development, a few B cells, expressing predominantly endogenous mu-chains, began the second stage of development in GALT. B cells were localized in and beneath the follicle-associated epithelium of GALT prior to B cell follicle formation, suggesting to us that B cell follicle formation is initiated near the follicle-associated epithelium, possibly through contact with intestinal microbiota. These IgH-Tg rabbits should provide a useful model for studies of B cell development both in bone marrow and in GALT.