The large size of many novel therapeutics impairs their transport through the tumor extracellular matrix and thus limits their therapeutic effectiveness. We propose that extracellular matrix composition, structure, and distribution determine the transport properties in tumors. Furthermore, because the characteristics of the extracellular matrix largely depend on the tumor-host interactions, we postulate that diffusion of macromolecules will vary with tumor type as well as anatomical location. Diffusion coefficients of macromolecules and liposomes in tumors growing in cranial windows (CWs) and dorsal chambers (DCs) were measured by fluorescence recovery after photobleaching. For the same tumor types, diffusion of large molecules was significantly faster in CW than in DC tumors. The greater diffusional hindrance in DC tumors was correlated with higher levels of collagen type I and its organization into fibrils. For molecules with diameters comparable to the interfibrillar space the diffusion was 5-to 10-fold slower in DC than in CW tumors. The slower diffusion in DC tumors was associated with a higher density of host stromal cells that synthesize and organize collagen type I. Our results point to the necessity of developing site-specific drug carriers to improve the delivery of molecular medicine to solid tumors.

Diffusion coefficients of tracer molecules in collagen type I gels prepared from 0-4.5% w/v solutions were measured by fluorescence recovery after photobleaching. When adjusted to account for in vivo tortuosity, diffusion coefficients in gels matched previous measurements in four human tumor xenografts with equivalent collagen concentrations. In contrast, hyaluronan solutions hindered diffusion to a lesser extent when prepared at concentrations equivalent to those reported in these tumors. Collagen permeability, determined from flow through gels under hydrostatic pressure, was compared with predictions obtained from application of the Brinkman effective medium model to diffusion data. Permeability predictions matched experimental results at low concentrations, but underestimated measured values at high concentrations. Permeability measurements in gels did not match previous measurements in tumors. Visualization of gels by transmission electron microscopy and light microscopy revealed networks of long collagen fibers at lower concentrations along with shorter fibers at high concentrations. Negligible assembly was detected in collagen solutions pregelation. However, diffusion was similarly hindered in pre and postgelation samples. Comparison of diffusion and convection data in these gels and tumors suggests that collagen may obstruct diffusion more than convection in tumors. These findings have significant implications for drug delivery in tumors and for tissue engineering applications.

Type 1 diabetes (T1D) animal models such as the nonobese diabetic (NOD) mouse have improved our understanding of disease pathophysiology, but many candidate therapeutics identified therein have failed to prevent/cure human disease. We have performed a comprehensive evaluation of disease-modifying agents tested in the NOD mouse based on treatment timing, duration, study length, and efficacy. Interestingly, some popular tenets regarding NOD interventions were not confirmed: all treatments do not prevent disease, treatment dose and timing strongly influence efficacy, and several therapies have successfully treated overtly diabetic mice. The analysis provides a unique perspective on NOD interventions and suggests that the response of this model to therapeutic interventions can be a useful predictor of the human response as long as careful consideration is given to treatment dose, timing, and protocols; more thorough investigation of these parameters should improve clinical translation.

Objective Quantitative assessment of disease activity in rheumatoid arthritis (RA) is important for patient management, and additional objective information may aid rheumatologists in clinical decision-making. We validated a recently-developed multi-biomarker disease activity (MBDA) test relative to clinical disease activity in diverse RA cohorts. Methods Serum samples were obtained from the InFoRM, BRASS, and Leiden Early Arthritis Clinic cohorts. Levels of 12 biomarkers were measured and combined according to a pre-specified algorithm to generate the composite MBDA score. The relationship of the MBDA score to clinical disease activity was characterized separately in seropositive and seronegative patients using Pearson correlations and area under the receiver operator characteristic curve (AUROC) to discriminate between patients with low and moderate/high disease activity. Associations between changes in MBDA score and clinical responses 6–12 weeks after initiation of anti-TNF or methotrexate treatment were evaluated by AUROC. Results The MBDA score was significantly associated with DAS28-CRP in both seropositive (AUROC=0.77; P<0.001) and seronegative patients (AUROC=0.70; P<0.001). In subgroups based on age, sex, body-mass index, and treatment, the MBDA score was associated with DAS28-CRP (P<0.05) in all seropositive and most seronegative subgroups. Changes in MBDA score at 6–12 weeks could discriminate both ACR50 responses (P=0.03) and DAS28-CRP improvement (P=0.002). Changes in MBDA score at 2 weeks were also associated with subsequent DAS28-CRP response (P=0.02). Conclusion Our findings establish the criterion and discriminant validity of a novel multi-biomarker test as an objective measure of RA disease activity to aid in the management of RA patients.

BackgroundDisease activity measurement is a key component of rheumatoid arthritis (RA) management. Biomarkers that capture the complex and heterogeneous biology of RA have the potential to complement clinical disease activity assessment.ObjectivesTo develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis.MethodsCandidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data. Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera. Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g. the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multivariate models. Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low vs. moderate/high disease activity. The effect of comorbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing.Results130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training. Multi-biomarker statistical models outperformed individual biomarkers at estimating disease activity. Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low vs. moderate/high clinical disease activity. Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography. The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100. No significant effects on the MBDA score were found for common comorbidities.ConclusionWe followed a stepwise approach to develop a quantitative serum-based measure of RA disease activity, based on 12-biomarkers, which was consistently associated with clinical disease activity levels.

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

KRAS- and BRAF-mutant tumors are often dependent on MAPK signaling for proliferation and survival and thus sensitive to MAPK pathway inhibitors. However, clinical studies have shown that MEK inhibitors are not uniformly effective in these cancers indicating that mutational status of these oncogenes does not accurately capture MAPK pathway activity. A number of transcripts are regulated by this pathway and are recurrently identified in genome-based MAPK transcriptional signatures. To test whether the transcriptional output of only 10 of these targets could quantify MAPK pathway activity with potential predictive or prognostic clinical utility, we created a MAPK Pathway Activity Score (MPAS) derived from aggregated gene expression. In vitro, MPAS predicted sensitivity to MAPK inhibitors in multiple cell lines, comparable to or better than larger genome-based statistical models. Bridging in vitro studies and clinical samples, median MPAS from a given tumor type correlated with cobimetinib (MEK inhibitor) sensitivity of cancer cell lines originating from the same tissue type. Retrospective analyses of clinical datasets showed that MPAS was associated with the sensitivity of melanomas to vemurafenib (HR: 0.596) and negatively prognostic of overall or progression-free survival in both adjuvant and metastatic CRC (HR: 1.5 and 1.4), adrenal cancer (HR: 1.7), and HER2+ breast cancer (HR: 1.6). MPAS thus demonstrates potential clinical utility that warrants further exploration.

Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes (“waterfall plot”). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.