Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target–drug combination with high potential for yielding clinical success within the efficacy–toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification.

This qualified model simulates the time course of ANC in the absence or presence of chemotherapy and predicts nadir, time to nadir and time of recovery from different grades of neutropenia upon treatment with filgrastim and pegfilgrastim.

Purpose To develop an integrated mechanism-based modeling approach for the interspecies scaling of pharmacokinetic (PK) and pharmacodynamic (PD) properties of type I interferons (IFNs) that exhibit target-mediated drug disposition (TMDD). Methods PK and PD profiles of human IFN-β1a, IFN-β1b, and IFN-α2a in humans, monkeys, rats, and mice from nine studies were extracted from the literature by digitization. Concentration-time profiles from different species were fitted simultaneously using various allometric relationships to scale model-specific parameters. Results PK/PD profiles of IFN-β1a in humans and monkeys were successfully characterized by utilizing the same rate constant parameters and scaling the volume of the central compartment to body weight using an allometric exponent of 1. Concentration and effect profiles of other IFNs were also well described by changing only the affinity of the drug to its receptor. PK profiles in rodents were simulated using an allometric exponent of −0.25 for the first-order elimination rate constant, and no receptor-binding was included given the lack of cross-reactivity. Conclusions An integrated TMDD PK/PD model was successfully combined with classic allometric scaling techniques and showed good predictive performance. Several parameters obtained from one IFN can be effectively shared to predict the kinetic behavior of other IFN subtypes.

Lu et al. show that tackling obesity with bispecific molecules that antagonize/ agonize GIPR/GLP-1R pathways decreases body weight and metabolic parameters in obese mice and monkeys. Mechanistic studies suggest that such molecules bind to GIPR and GLP-1R simultaneously and trigger receptor internalization, amplifying endosomal cAMP signaling in cells expressing both receptors.

Trastuzumab emtansine (T-DM1) is an antibody–drug conjugate (ADC) composed of multiple molecules of the antimicrotubule agent DM1 linked to trastuzumab, a humanized anti–human epidermal growth factor receptor 2 (HER2) monoclonal antibody. Pharmacokinetics data from phase I (n = 52) and phase II (n = 111) studies in HER2-positive metastatic breast cancer patients show a shorter terminal half-life for T-DM1 than for total trastuzumab (TTmAb). In this work, we translated prior preclinical modeling in monkeys to develop a semi-mechanistic population pharmacokinetics model to characterize T-DM1 and TTmAb concentration profiles. A series of transit compartments with the same disposition parameters was used to describe the deconjugation process from higher to lower drug-to-antibody ratios (DARs). The structure could explain the shorter terminal half-life of T-DM1 relative to TTmab. The final model integrates prior knowledge of T-DM1 DARs from preclinical studies and could provide a platform for understanding and characterizing the pharmacokinetics of other ADC systems.