Multiscale Modeling in the Clinic: Drug Design and Development

Abstract

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Keywords: Drug delivery; Mathematical; Multiscale modeling; Pharmacology; Simulation.

Figure 1

Multiscale computational models can span an enormous spatial range from populations downward and time scales from sub-second to decades.

Figure 2

A multiscale computational model for predicting cardiac pharmacology. The model may allow simulation and prediction from the small molecule scale of the drug, to protein structure, protein function, cell and tissue levels.

Figure 3

A sketch of the ABM model for the impact of the stiffness of the BMSC-formed MIC niches to the myeloma lineage expansion and drug responses. (ABM: agent-based model; BMSC: bone marrow stromal cell; MIC: myeloma initiating cell; PC: cancer progenitor cell; MM: multiple myeloma cell; TMM: terminal multiple myeloma cell; BZM: Bortezomib; AMD: AMD3100; CXCR4: C-X-C chemokine receptor type 4; SDF1: stromal cell-derived factor 1)

Figure 4

Figure 5

Schematic illustration of intracellular and intercellular signaling and cellular dynamics in bone healing and bone remodeling. Bone regeneration or bone remodeling involves bone resorption by osteoclasts (OC) and the following bone formation by osteoblasts (OB) within basic multi-cellular units (BMU). Three cytokines were considered: TGFβ, Wnt and BMP2. Intracellular signaling pathway consists of Smad2/3, Smad1/5, β-Catenin, and Runx2 and Osx. Runx2 can promote the differentiation of mesenchymal stem cells (MSCs) into pre-osteoblasts (OBp) and can inhibit the differentiation of pre-osteoblasts into active osteoblasts (OBa). Osx also play a promoting role in the later stage of osteoblastic lineage which interacts with osteoclastic lineage through intercellular signaling pathway RANK-RANKL-OPG.

Figure 6

Synergy prediction on dual combinations of Wnt, BMP2, and TGFβ based on Bliss combination index. Wnt and BMP2 perform dose-dependent synergism. (a) BMP2 levels governed the synergism. When the BMP2 level was higher than 0, the two drugs were synergic, otherwise antagonistic. We also found that Wnt at high levels showed opposite effects in terms of synergism at different BMP2 levels. When BMP2 level was high, increasing Wnt level promoted the synergistic effects of the two drugs. In contrast, when BMP2 level was low, the more the Wnt was introduced, the stronger the antagonistic effect was. (b) Wnt/TGFβ and (c) BMP2/TGFβ combinations also showed dose-dependent synergism but much lower responses.

Figure 7

Schematics demonstration of drug transport (A) through the circulation system, (B) blood vasculature, (C) across the vessel wall, (D) through interstitial space to the tumor site.

Figure 8

Overview of the multiple scales and classes of processes involved in host-microbial interactions. Modeling efforts would necessarily integrate metabolic processes, microbial community dynamics, host responses involving inflammation as they manifest in different tissues. Figure reproduced from under the Creative Commons Attribution License.

Figure 9

The computational systems chemical biology (SCB) workflow integrates cheminformatics platforms for model-based identification of small molecule therapeutics with dynamic simulation of the system scale outcome of targeted inhibition. The SCB approach has been used to theoretically investigate the metabolic consequence of multiple chemotherapeutic agents on persistent and nonpersistent M. tuberculosis.

Figure 10

Proposed computer-aided vaccine design workflow.

Figure 11

Snapshots of the initial and final configurations of (a) human papillomavirus (HPV) T=1 virus like particle (VLP) undergoing thermal fluctuations. After 29 ns, the system has slightly changed signifying the VLP is stable and has reached equilibrium, and (b) P22 T=1 VLP significantly evolving from its initial symmetrical icosahedral form, which implies the VLP is unstable.

Figure 12

Experimentally determined immunogenicity is inversely correlated with calculated MSF, a measure of flexibility.