Characterization of the Pharmacokinetic and Pharmacodynamic Profile of Apraglutide, a Glucagon-Like Peptide-2 Analog, in Healthy Volunteers.

Apraglutide (FE 203799) is a glucagon-like peptide-2 (GLP-2) analog under development for the treatment of intestinal failure associated with short bowel syndrome (SBS-IF) and graft-versus-host disease (GvHD). Compared with native GLP-2, apraglutide has slower absorption, reduced clearance, and higher protein binding, enabling once-weekly dosing. This study evaluated the pharmacokinetic (PK) and pharmacodynamic (PD) profile of apraglutide in healthy adults. Healthy volunteers were randomized to receive 6 weekly subcutaneous administrations of 1, 5, or 10 mg apraglutide or placebo. PK and citrulline (an enterocyte mass PD marker) samples were collected at multiple time points. Kinetic parameters of apraglutide and citrulline were calculated using noncompartmental analysis; repeated PD measures were analyzed with a mixed model of covariance. A population PK/PD model was developed that also included data from a previous phase 1 study in healthy volunteers. Twenty-four subjects were randomized; 23 received all study drug administrations. Mean estimated apraglutide clearance was 16.5-20.7 l/day, and mean volume of distribution was 55.4-105.0 liters. A dose-dependent increase in citrulline plasma concentration was observed, with 5-mg and 10-mg doses inducing higher citrulline levels than 1-mg doses and placebo. PK/PD analysis showed that weekly 5-mg apraglutide induced the maximal citrulline response. Increased plasma citrulline levels were sustained for 10-17 days after the final apraglutide administration. Apraglutide displays predictable dose-dependent PK and PD profiles, with a 5-mg dose showing significant PD effects. Results suggest that apraglutide has early and enduring effects on enterocyte mass and supports the continued development of weekly subcutaneous apraglutide for SBS-IF and GvHD patient populations. SIGNIFICANCE STATEMENT: Once-weekly subcutaneous apraglutide results in dose-dependent elevations of plasma citrulline (an enterocyte mass pharmacodynamic marker) with parameters suggesting that apraglutide has lasting effects on enterocyte mass and the potential to provide therapeutic benefits. This is the first report of a model relating glucagon-like peptide-2 (GLP-2) agonism and its effects in intestinal mucosa, affording not only the ability to predict pharmacologic effects of GLP-2 analogs but also the exploration of optimal dosing regimens for this drug class across populations with different body weights.

Signal integration by the CYP1A1 promoter--a quantitative study.

Genes involved in detoxification of foreign compounds exhibit complex spatiotemporal expression patterns in liver. Cytochrome P450 1A1 (CYP1A1), for example, is restricted to the pericentral region of liver lobules in response to the interplay between aryl hydrocarbon receptor (AhR) and Wnt/ß-catenin signaling pathways. However, the mechanisms by which the two pathways orchestrate gene expression are still poorly understood. With the help of 29 mutant constructs of the human CYP1A1 promoter and a mathematical model that combines Wnt/ß-catenin and AhR signaling with the statistical mechanics of the promoter, we systematically quantified the regulatory influence of different transcription factor binding sites on gene induction within the promoter. The model unveils how different binding sites cooperate and how they establish the promoter logic; it quantitatively predicts two-dimensional stimulus-response curves. Furthermore, it shows that crosstalk between Wnt/ß-catenin and AhR signaling is crucial to understand the complex zonated expression patterns found in liver lobules. This study exemplifies how statistical mechanical modeling together with combinatorial reporter assays has the capacity to disentangle the promoter logic that establishes physiological gene expression patterns.

Computer-assisted curation of a human regulatory core network from the biological literature.

MOTIVATION: A highly interlinked network of transcription factors (TFs) orchestrates the context-dependent expression of human genes. ChIP-chip experiments that interrogate the binding of particular TFs to genomic regions are used to reconstruct gene regulatory networks at genome-scale, but are plagued by high false-positive rates. Meanwhile, a large body of knowledge on high-quality regulatory interactions remains largely unexplored, as it is available only in natural language descriptions scattered over millions of scientific publications. Such data are hard to extract and regulatory data currently contain together only 503 regulatory relations between human TFs. RESULTS: We developed a text-mining-assisted workflow to systematically extract knowledge about regulatory interactions between human TFs from the biological literature. We applied this workflow to the entire Medline, which helped us to identify more than 45 000 sentences potentially describing such relationships. We ranked these sentences by a machine-learning approach. The top-2500 sentences contained ∼900 sentences that encompass relations already known in databases. By manually curating the remaining 1625 top-ranking sentences, we obtained more than 300 validated regulatory relationships that were not present in a regulatory database before. Full-text curation allowed us to obtain detailed information on the strength of experimental evidences supporting a relationship. CONCLUSIONS: We were able to increase curated information about the human core transcriptional network by >60% compared with the current content of regulatory databases. We observed improved performance when using the network for disease gene prioritization compared with the state-of-the-art. AVAILABILITY AND IMPLEMENTATION: Web-service is freely accessible at http://fastforward.sys-bio.net/. CONTACT: leser@informatik.hu-berlin.de or nils.bluethgen@charite.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimization of Cancer Treatment in the Frequency Domain.

Thorough exploration of alternative dosing frequencies is often not performed in conventional pharmacometrics approaches. Quantitative systems pharmacology (QSP) can provide novel insights into optimal dosing regimen and drug behaviors which could add a new dimension to the design of novel treatments. However, methods for such an approach are currently lacking. Recently, we illustrated the utility of frequency-domain response analysis (FdRA), an analytical method used in control engineering, using several generic pharmacokinetic-pharmacodynamic case studies. While FdRA is not applicable to models harboring ever increasing variables such as those describing tumor growth, studying such models in the frequency domain provides valuable insight into optimal dosing frequencies. Through the analysis of three distinct tumor growth models (cell cycle-specific, metronomic, and acquired resistance), we demonstrate the application of a simulation-based analysis in the frequency domain to optimize cancer treatments. We study the response of tumor growth to dosing frequencies while simultaneously examining treatment safety, and found for all three models that above a certain dosing frequency, tumor size is insensitive to an increase in dosing frequency, e.g., for the cell cycle-specific model, one dose per 3 days, and an hourly dose yield the same reduction of tumor size to 3% of the initial size after 1 year of treatment. Additionally, we explore the effect of drug elimination rate changes on the tumor growth response. In summary, we show that the frequency-domain view of three models of tumor growth dynamics can help in optimizing drug dosing regimen to improve treatment success.

Outside-In Systems Pharmacology Combines Innovative Computational Methods With High-Throughput Whole Vertebrate Studies.

To advance the systems approach in pharmacology, experimental models and computational methods need to be integrated from early drug discovery onward. Here, we propose outside-in model development, a model identification technique to understand and predict the dynamics of a system without requiring prior biological and/or pharmacological knowledge. The advanced data required could be obtained by whole vertebrate, high-throughput, low-resource dose-exposure-effect experimentation with the zebrafish larva. Combinations of these innovative techniques could improve early drug discovery.

Frequency-Domain Response Analysis for Quantitative Systems Pharmacology Models.

Drug dosing regimen can significantly impact drug effect and, thus, the success of treatments. Nevertheless, trial and error is still the most commonly used method by conventional pharmacometric approaches to optimize dosing regimen. In this tutorial, we utilize four distinct classes of quantitative systems pharmacology models to introduce frequency-domain response analysis, a method widely used in electrical and control engineering that allows the analytical optimization of drug treatment regimen from the dynamics of the model.

From reaction networks to information flow--using modular response analysis to track information in signaling networks.

Even if the biochemical details of signaling networks are known, it is often hard to track how information flows through the network. In combination with experimental techniques, modular response analysis has proven useful in analyzing the quantitative information transfer in signal transduction networks. The sensitivity of a target (e.g., transcription factor, protein) to an upstream stimulus (e.g., growth factor) can be determined by a so-called response coefficient. We have used this methodology to analyze how information flows in networks where the details of the mechanisms in the networks are known, but parameters are lacking. Using a Monte Carlo approach, we apply this method to track the routes of information flow. More specifically, we determine whether a given species has no, positive or negative influence on any other species in the network. Surprisingly, one can uniquely determine whether a molecule activates or inhibits another one in more than 99% of the interactions solely from the topology of the reaction network. To exemplify the methodology, we briefly discuss three signaling networks of different complexity: (i) a Wnt signaling pathway model with 15 species, (ii) a MAPK signaling pathway model with 200 species, and (iii) a large-scale signaling network of the entire cell with over 6000 species.