Claus Bendtsen

Interplay between α-, β-, and γ-Secretases Determines Biphasic Amyloid-β Protein Level in the Presence of a γ-Secretase Inhibitor

Background: Moderate concentrations of γ-secretase inhibitor increase Aβ production in different scenarios from cell lines to humans. Results: A mathematical model, including α-, β-, and γ-secretases, is proposed describing Aβ rise. Conclusion: The Aβ rise is decided by the interplay between the three secretases and not γ-secretase alone. Significance: This has important implications for the development of drugs targeting Aβ production in Alzheimer disease. Amyloid-β (Aβ) is produced by the consecutive cleavage of amyloid precursor protein (APP) first by β-secretase, generating C99, and then by γ-secretase. APP is also cleaved by α-secretase. It is hypothesized that reducing the production of Aβ in the brain may slow the progression of Alzheimer disease. Therefore, different γ-secretase inhibitors have been developed to reduce Aβ production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in Aβ production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the Aβ rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the Aβ dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in Aβ production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by β-secretase, leading to an additional increase in C99 that overcomes the loss in γ-secretase activity. With a minor extension, the model also describes plasma Aβ profiles observed in humans upon dosing with a γ-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting Aβ production in Alzheimer disease.

Modeling RAS phenotype in colorectal cancer uncovers novel molecular traits of RAS dependency and improves prediction of response to targeted agents in patients

Purpose: KRAS wild-type status is an imperfect predictor of sensitivity to anti-EGF receptor (EGFR) monoclonal antibodies in colorectal cancer, motivating efforts to identify novel molecular aberrations driving RAS. This study aimed to build a quantitative readout of RAS pathway activity to (i) uncover molecular surrogates of RAS activity specific to colorectal cancer, (ii) improve the prediction of cetuximab response in patients, and (iii) suggest new treatment strategies. Experimental Design: A model of RAS pathway activity was trained in a large colorectal cancer dataset and validated in three independent colorectal cancer patient datasets. Novel molecular traits were inferred from The Cancer Genome Atlas colorectal cancer data. The ability of the RAS model to predict resistance to cetuximab was tested in mouse xenografts and three independent patient cohorts. Drug sensitivity correlations between our model and large cell line compendiums were performed. Results: The performance of the RAS model was remarkably robust across three validation datasets. (i) Our model confirmed the heterogeneity of the RAS phenotype in KRAS wild-type patients, and suggests novel molecular traits driving its phenotype (e.g., MED12 loss, FBXW7 mutation, MAP2K4 mutation). (ii) It improved the prediction of response and progression-free survival (HR, 2.0; P < 0.01) to cetuximab compared with KRAS mutation (xenograft and patient cohorts). (iii) Our model consistently predicted sensitivity to MAP–ERK kinase (MEK) inhibitors (P < 0.01) in two cell panel screens. Conclusions: Modeling the RAS phenotype in colorectal cancer allows for the robust interrogation of RAS pathway activity across cell lines, xenografts, and patient cohorts. It demonstrates clinical utility in predicting response to anti-EGFR agents and MEK inhibitors. Clin Cancer Res; 20(1); 265–72. ©2013 AACR.

X-ray computed tomography: semiautomated volumetric analysis of late-stage lung tumors as a basis for response assessments

Background. This study presents a semiautomated approach for volumetric analysis of lung tumors and evaluates the feasibility of using volumes as an alternative to line lengths as a basis for response evaluation criteria in solid tumors (RECIST). The overall goal for the implementation was to accurately, precisely, and efficiently enable the analyses of lesions in the lung under the guidance of an operator. Methods. An anthropomorphic phantom with embedded model masses and 71 time points in 10 clinical cases with advanced lung cancer was analyzed using a semi-automated workflow. The implementation was done using the Cognition Network Technology. Results. Analysis of the phantom showed an average accuracy of 97%. The analyses of the clinical cases showed both intra- and interreader variabilities of approximately 5% on average with an upper 95% confidence interval of 14% and 19%, respectively. Compared to line lengths, the use of volumes clearly shows enhanced sensitivity with respect to determining response to therapy. Conclusions. It is feasible to perform volumetric analysis efficiently with high accuracy and low variability, even in patients with late-stage cancer who have complex lesions.

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.

Visualization and quantitation of GLUT4 translocation in human skeletal muscle following glucose ingestion and exercise

Insulin‐ and contraction‐stimulated increases in glucose uptake into skeletal muscle occur in part as a result of the translocation of glucose transporter 4 (GLUT4) from intracellular stores to the plasma membrane (PM). This study aimed to use immunofluorescence microscopy in human skeletal muscle to quantify GLUT4 redistribution from intracellular stores to the PM in response to glucose feeding and exercise. Percutaneous muscle biopsy samples were taken from the m. vastus lateralis of ten insulin‐sensitive men in the basal state and following 30 min of cycling exercise (65% VO2 max). Muscle biopsy samples were also taken from a second cohort of ten age‐, BMI‐ and VO2 max‐matched insulin‐sensitive men in the basal state and 30 and 60 min following glucose feeding (75 g glucose). GLUT4 and dystrophin colocalization, measured using the Pearsons correlation coefficient, was increased following 30 min of cycling exercise (baseline r = 0.47 ± 0.01; post exercise r = 0.58 ± 0.02; P < 0.001) and 30 min after glucose ingestion (baseline r = 0.42 ± 0.02; 30 min r = 0.46 ± 0.02; P < 0.05). Large and small GLUT4 clusters were partially depleted following 30 min cycling exercise, but not 30 min after glucose feeding. This study has, for the first time, used immunofluorescence microscopy in human skeletal muscle to quantify increases in GLUT4 and dystrophin colocalization and depletion of GLUT4 from large and smaller clusters as evidence of net GLUT4 translocation to the PM.

The Role of Historical Bioactivity Data in the Deconvolution of Phenotypic Screens.

A substantial challenge in phenotypic drug discovery is the identification of the molecular targets that govern a phenotypic response of interest. Several experimental strategies are available for this, the so-called target deconvolution process. Most of these approaches exploit the affinity between a small-molecule compound and its putative targets or use large-scale genetic manipulations and profiling. Each of these methods has strengths but also limitations such as bias toward high-affinity interactions or risks from genetic compensation. The use of computational methods for target and mechanism of action identification is a complementary approach that can influence each step of a phenotypic screening campaign. Here, we describe how cheminformatics and bioinformatics are embedded in the process from initial selection of a focused compound library from a large set of historical small-molecule screens through the analysis of screening results. We present a deconvolution method based on enrichment analysis and using known bioactivity data of screened compounds to infer putative targets, pathways, and biological processes that are consistent with the observed phenotypic response. As an example, the approach is applied to a cellular screen aiming at identifying inhibitors of tumor necrosis factor–α production in lipopolysaccharide-stimulated THP-1 cells. In summary, we find that the approach can contribute to solving the often very complex target deconvolution task.

Rational cell culture optimization enhances experimental reproducibility in cancer cells

Optimization of experimental conditions is critical in ensuring robust experimental reproducibility. Through detailed metabolomic analysis we found that cell culture conditions significantly impacted on glutaminase (GLS1) sensitivity resulting in variable sensitivity and irreproducibility in data. Baseline metabolite profiling highlighted that untreated cells underwent significant changes in metabolic status. Both the extracellular levels of glutamine and lactate and the intracellular levels of multiple metabolites changed drastically during the assay. We show that these changes compromise the robustness of the assay and make it difficult to reproduce. We discuss the implications of the cells’ metabolic environment when studying the effects of perturbations to cell function by any type of inhibitor. We then devised ‘metabolically rationalized standard’ assay conditions, in which glutaminase-1 inhibition reduced glutamine metabolism differently in both cell lines assayed, and decreased the proliferation of one of them. The adoption of optimized conditions such as the ones described here should lead to an improvement in reproducibility and help eliminate false negatives as well as false positives in these assays.

Improving machine learning in early drug discovery

The high cost for new medicines is hindering their development and machine learning is therefore being used to avoid carrying out physical experiments. Here, we present a comparison between three different machine learning approaches in a classification setting where learning and prediction follow a teaching schedule to mimic the drug discovery process. The approaches are standard SVM classification, SVM based multi-kernel classification and SVM classification based on learning using privileged information. Our two main conclusions are derived using experimental in-vitro data and compound structure descriptors. The in-vitro data is assumed to i) be completely absent in the standard SVM setting, ii) be available at all times when applying multi-kernel learning, or iii) be available as privileged information during training only. The structure descriptors are always available. One conclusion is that multi-kernel learning has higher odds than standard SVM in producing higher accuracy. The second is that learning using privileged information does not have higher odds than the standard SVM, although it may improve accuracy when the training sets are small.

Kinase inhibition leads to hormesis in a dual phosphorylation-dephosphorylation cycle

Many antimicrobial and anti-tumour drugs elicit hormetic responses characterised by low-dose stimulation and high-dose inhibition. While this can have profound consequences for human health, with low drug concentrations actually stimulating pathogen or tumour growth, the mechanistic understanding behind such responses is still lacking. We propose a novel, simple but general mechanism that could give rise to hormesis in systems where an inhibitor acts on an enzyme. At its core is one of the basic building blocks in intracellular signalling, the dual phosphorylation-dephosphorylation motif, found in diverse regulatory processes including control of cell proliferation and programmed cell death. Our analytically-derived conditions for observing hormesis provide clues as to why this mechanism has not been previously identified. Current mathematical models regularly make simplifying assumptions that lack empirical support but inadvertently preclude the observation of hormesis. In addition, due to the inherent population heterogeneities, the presence of hormesis is likely to be masked in empirical population-level studies. Therefore, examining hormetic responses at single-cell level coupled with improved mathematical models could substantially enhance detection and mechanistic understanding of hormesis.

Modeling the Effect of Mucin Binding in the Gut on Drug Delivery

An important part the absorption, distribution, metabolism and excretion of an oral therapeutic is the flux rate of drug compound crossing the mucus lining of the gut. To understand this part of the absorption process, we develop a mathematical model of advection, diffusion and binding of drug compounds within the mucus layer of the intestines. Analysis of this model yields simple, measurable criteria for the successful mucin layer traversal of drug compound.