EwaldSolidSolution: A High-Throughput Application to Quickly Sample Stable Site Arrangements for Ionic Solid Solutions.

Data-driven materials design of ionic solid solutions often requires sampling (meta)stable site arrangements among the massive number of possibilities, which has been hampered by the lack of relevant methods. Herein, we develop a quick high-throughput sampling application for site arrangements of ionic solid solutions. Given the Ewald Coulombic energies for an initial site arrangement, EwaldSolidSolution updates the modified parts of the energy with varying sites only, which can be exhaustively estimated by using massively parallel processing. Given two representative examples of solid electrolytes, Li10GeP2S12 and Na3Zr2Si2PO12, EwaldSolidSolution successfully calculates the Ewald Coulombic energies of 211,266,225 (235,702,467) site arrangements for Li10GeP2S12 (Na3Zr2Si2PO12) with 216 (160) ion sites per unit cell in 1223.2 (1187.9) seconds: 0.0057898 (0.0050397) milliseconds per site arrangement. The computational cost is enormously saved in comparison with an existing application, which estimates the energy of a site arrangement on the second timescale. The positive correlations between the Ewald Coulombic energies and those estimated by density functional theory calculations show that (meta)stable samples are easily revealed by our computationally inexpensive algorithm. We also reveal that the different-valence nearest-neighbor pairs are distinctively formed in the low-energy site arrangements. EwaldSolidSolution will boost the materials design of ionic solid solutions by attracting broad interest.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Targeted Fusogenic Liposomes for Effective Tumor Delivery and Penetration of Lipophilic Cargoes.

Nanoparticle-based drug delivery has been widely used for effective anticancer treatment. However, a key challenge restricting the efficacy of nanotherapeutics is limited tissue penetration within solid tumors. Here, we report a targeted fusogenic liposome (TFL) that can selectively deliver lipophilic cargo to the plasma membranes of tumor cells. TFL is prepared by directly attaching tumor-targeting peptides to the surface of FL instead of the cationic moieties. The lipophilic cargo loaded in the membrane of TFL is transferred to the plasma membranes of tumor cells and subsequently packaged in the extracellular vesicles (EVs) released by the cells. Systemically administered TFL accumulates in the perivascular region of tumors, where the lipophilic cargo is unloaded to the tumor cell membranes and distributed autonomously throughout the tumor tissue via extracellular vesicle-mediated intercellular transfer. When loaded with a lipophilic pro-apoptotic drug, thapsigargin (Tg), TFL significantly inhibits tumor growth in a mouse colorectal cancer model. Furthermore, the combination treatment with TFL (Tg) potentiates the antitumor efficacy of FDA-approved liposomal doxorubicin, whose therapeutic effect is limited to perivascular regions without significant toxicity.

Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering.

Many clinical trials for cancer precision medicine have yielded unsatisfactory results due to challenges such as drug resistance and low efficacy. Drug resistance is often caused by the complex compensatory regulation within the biomolecular network in a cancer cell. Recently, systems biological studies have modeled and simulated such complex networks to unravel the hidden mechanisms of drug resistance and identify promising new drug targets or combinatorial or sequential treatments for overcoming resistance to anticancer drugs. However, many of the identified targets or treatments present major difficulties for drug development and clinical application. Nanocarriers represent a path forward for developing therapies with these "undruggable" targets or those that require precise combinatorial or sequential application, for which conventional drug delivery mechanisms are unsuitable. Conversely, a challenge in nanomedicine has been low efficacy due to heterogeneity of cancers in patients. This problem can also be resolved through systems biological approaches by identifying personalized targets for individual patients or promoting the drug responses. Therefore, integration of systems biology and nanomaterial engineering will enable the clinical application of cancer precision medicine to overcome both drug resistance of conventional treatments and low efficacy of nanomedicine due to patient heterogeneity.

Interdependent effect of P-glycoprotein-mediated drug efflux and intracellular drug binding on intracellular paclitaxel pharmacokinetics: application of computational modeling.

Intracellular concentration of paclitaxel is determined by the extracellular drug concentration, the level of the mdr1 P-glycoprotein (Pgp), and binding to intracellular proteins including tubulins/microtubules. The present study used a computational method to examine the effects of these factors, singly and in combination, on intracellular paclitaxel pharmacokinetics. The study was performed using our previously described intracellular pharmacokinetic model. The parameters representing Pgp-mediated drug efflux and intracellular drug binding (i.e., number of Pgp and binding sites and binding affinity) were altered systematically and used to generate computer simulations depicting the intracellular paclitaxel pharmacokinetics at clinically relevant extracellular (e.g., plasma) drug concentrations. The simulation results indicate that all four factors played a role in determining the intracellular drug accumulation. The rank order of the importance of these parameters was extracellular drug concentration >> intracellular binding capacity > intracellular binding affinity > Pgp expression. The results further showed that omission of one or more of these factors in the experimental design would lead to erroneous conclusions on the importance of other factors, as simultaneous changes in more than one parameter altered the relative importance and offset the effects of other parameters. In summary, results of the present study demonstrate the use of computational modeling to depict the effects of biological parameters such as drug efflux transporters, drug binding sites, and binding affinity on intracellular accumulation and retention of drugs that bind to cellular components.

Drug delivery and transport to solid tumors.

PURPOSE: The purpose of this review is to provide an overview of the principles of and barriers to drug transport and delivery to solid tumors. METHODS: This review consists of four parts. Part I provides an overview of the differences in the vasculature in normal and tumor tissues, and the relationship between tumor vasculature and drug transport. Part II describes the determinants of transport of drugs and particles across tumor vasculature into surrounding tumor tissues. Part III discusses the determinants and barriers of drug transport, accumulation, and retention in tumors. Part IV summarizes the experimental approaches used to enhance drug delivery and transport in solid tumors. RESULTS: Drug delivery to solid tumors consists of multiple processes, including transport via blood vessels, transvascular transport, and transport through interstitial spaces. These processes are dynamic and change with time and tumor properties and are affected by multiple physicochemical factors of a drug, multiple tumor biologic factors, and as a consequence of drug treatments. The biologic factors, in turn, have opposing effects on one or more processes in the delivery of drugs to solid tumors. CONCLUSION: The effectiveness of cancer therapy depends in part on adequate delivery of the therapeutic agents to tumor cells. A better understanding of the processes and contribution of these factors governing drug delivery may lead to new cancer therapeutic strategies.