Molecular Systems Biology in Drug Development

A fluorescent imaging method for analyzing the biodistribution of therapeutic monoclonal antibodies that can distinguish intact antibodies from their breakdown products

Many monoclonal antibodies have been developed for therapy over the last 2 decades. In the development of therapeutic antibodies, the preclinical assessment of an antibody's biodistribution is important for the prediction of the antibody's efficacy and safety. For imaging analyses of such biodistributions, radioisotope (RI) labeling and fluorescence labeling methods are typically used, but the resulting data are limited because these methods cannot distinguish breakdown products from intact antibodies. To resolve this problem, we developed a novel method using fluorescent resonance energy transfer (FRET)-type labeling and a spectral unmixing tool. With FRET-type labeling (labeling with 2 species of fluorophore), different fluorescence properties of labeled intact antibodies and their breakdown products (the hydrolyzed/digested type of breakdown products) are made visible. With the spectral unmixing tool, the fluorescence of a solution containing the intact antibody and its breakdown products could be unmixed in proportion to their contents. Moreover, when labeled antibodies that targeted either human epidermal growth factor receptor-2 or epidermal growth factor receptor were injected into nude mice implanted subcutaneously with tumor cells, the accumulation of the injected labeled antibodies and their breakdown products in the tumor could be separately analyzed by both whole-mouse imaging and a tumor homogenate analysis. These results suggest that our method using FRET-type labeling and a spectral unmixing tool could be useful in distinguishing breakdown products from intact antibodies.

Mathematical modeling in cancer drug discovery

Mathematical models have the potential to help discover new therapeutic targets and treatment strategies. In this review, we discuss how the latest developments in mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations. We give special attention to two modeling approaches: network-based modeling and multiscale modeling, because they have begun to show promise in facilitating the process of effective cancer drug discovery. Both modeling approaches are integrated with a variety of experimental methods to ensure proper parameterization and to maximize their predictive value. We also discuss several challenges faced in modeling-based drug discovery.

Maximizing the information content of experiments in systems biology

Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.

For most biological signalling and regulatory systems we still lack reliable mechanistic models. And where such models exist, e.g. in the form of differential equations, we typically have only rough estimates for the parameters that characterize the biochemical reactions. In order to improve our knowledge of such systems we require better estimates for these parameters and here we show how judicious choice of experiments, based on a combination of simulations and information theoretical analysis, can help us. Our approach builds on the available, frequently rudimentary information, and identifies which experimental set-up provides most additional information about all the parameters, or individual parameters. We will also consider the related but subtly different problem of which experiments need to be performed in order to decrease the uncertainty about the behaviour of the system under altered conditions. We develop the theoretical framework in the necessary detail before illustrating its use and applying it to the repressilator model, the regulation of Hes1 and signal transduction in the Akt pathway.

Kaposi's sarcoma: a computational approach through protein-protein interaction and gene regulatory networks analysis

Interactomic data for Kaposi's Sarcoma Associated Herpes virus (KSHV)-the causative agent of vascular origin tumor called Kaposi's sarcoma-is relatively modest to date. The objective of this study was to assign functions to the previously uncharacterized ORFs in the virus using computational approaches and subsequently fit them to the host interactome landscape on protein, gene, and cellular level. On the basis of expression data, predicted RNA interference data, reported experimental data, and sequence based functional annotation we also tried to hypothesize the ORFs role in lytic and latent cycle during viral infection. We studied 17 previously uncharacterized ORFs in KSHV and the host-virus interplay seems to work in three major functional pathways-cell division, transport, metabolic and enzymatic in general. Studying the host-virus crosstalk for lytic phase predicts ORF 10 and ORF 11 as a predicted virus hub whereas PCNA is predicted as a host hub. On the other hand, ORF31 has been predicted as a latent phase inducible protein. KSHV invests a lion's share of its coding potential to suppress host immune response; various inflammatory mediators such as IFN-γ, TNF, IL-6, and IL-8 are negatively regulated by the ORFs while Il-10 secretion is stimulated in contrast. Although, like any other computational prediction, the study requires further validation, keeping into account the reproducibility and vast sample size of the systems biology approach the study allows us to propose an integrated network for host-virus interaction with good confidence. We hope that the study, in the long run, would help us identify effective dug against potential molecular targets.

Clinical pharmacology: a paradigm for individualized medicine

Individualized medicine provides a powerful engine revolutionizing the practice of clinical pharmacology, tailoring genetic and molecular profiles of patients to improve therapeutic specificity, reduce treatment variability and minimize adverse drug events. In that context, advances in individualized medicine have transformed the science of clinical pharmacology and therapeutics from drug discovery through identification of drugable targets, development through stratification of disease risk, regulation through identifying pathways mediating off-target effects and utilization through personalizing drug regimens. This revolution in fundamental and applied therapeutics has entrained an evolution in biology and medicine. Insights in the mechanistic basis of cell, tissue and organ function, and their interface with the environment are being translated to define disease risk, identify processes mediating disease susceptibility, target mechanism-based therapies, and tailor prevention and control paradigms, providing previously unanticipated opportunities for patient-specific disease management. The emerging field of individualized medicine is transforming the practice of clinical pharmacology, driving the leading edge of discovery from the laboratory bench to the evidence basis of practice in the clinic, extending to populations, to transform healthcare and create predictive, personalized and pre-emptive solutions for tailored patient-specific therapeutic strategies.

Bioinformatic networks: molecular reticles for pinpointing pharmacological target selection

Bridging centrality was developed as a novel metric to capitalize on rich biological networks and enable selection of therapeutically efficient, druggable candidates. Through incorporation of local and global network properties, targetable nodes are identified that harbor latent capacity to intercept informational flow without compromising systemic functionality. Translational implementation holds potential for increased therapeutic specificity and offers opportunities to add value to drug discovery and development through facilitation of individualized treatment.