Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization

Concentration optimization of combinatorial drugs using Markov chain-based models

BACKGROUND

Combinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs.

RESULTS

In this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments.

CONCLUSION

This article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy.

Harnessing a Novel Machine-Learning-Assisted Evolutionary Algorithm to Co-optimize Three Characteristics of an Electrospun Oil Sorbent

The optimization of materials is challenging as it often involves simultaneous manipulation of an assembly of condition parameters, which generates an enormous combinational space. Thus, optimization models and algorithms are widely adopted to accelerate material design and optimization. However, most optimization strategies can poorly handle multiple parameters simultaneously with limited prior knowledge. Herein, we describe a novel systematic optimization strategy, namely, machine-learning-assisted differential evolution, which combines machine learning and the evolutionary algorithm together, for zero-prior-data, rapid, and simultaneous optimization of multiple objectives. The strategy enables the evolutionary algorithm to "learn" so as to accelerate the optimization process, and also to identify quantitative interactions between the condition parameters and functional characteristics of the material. The performance of the strategy is verified by in silico simulations, as well as an application on simultaneously optimizing three characteristics, namely, water contact angle, oil absorption capacity, and mechanical strength, of an electrospun polystyrene/polyacrylonitrile (PS/PAN) material as a potential sorbent for a marine oil spill. With only 50 tests, the optimal fabrication parameters were successfully located from a combinatorial space of 50 000 possibilities. The presented platform technique offers a universal enabling technology to identify the optimal conditions rapidly from a daunting parameter space to synthesize materials with multiple desired functionalities.

Stimuli-Responsive Nanodiamond-Based Biosensor for Enhanced Metastatic Tumor Site Detection

Metastasis is often critical to cancer progression and linked to poor survival and drug resistance. Early detection of metastasis, as well as identification of metastatic tumor sites, can improve cancer patient survival. Thus, developing technology to improve the detection of cancer metastasis biomarkers can improve both diagnosis and treatment. In this study, we investigated the use of nanodiamonds to develop a stimuli-responsive metastasis detection complex that utilizes matrix metalloproteinase 9 (MMP9) as a metastasis biomarker, as MMP9 increased expression has been shown to be indicative of metastasis. The nanodiamond-MMP9 biosensor complex consists of nanodiamonds functionalized with MMP9-specific fluorescent-labeled substrate peptides. Using this design, protease activity of MMP9 can be accurately measured and correlated to MMP9 expression. The nanodiamond-MMP9 biosensor also demonstrated an enhanced ability to protect the base sensor peptide from nonspecific serum protease cleavage. This enhanced peptide stability, combined with a quantitative stimuli-responsive output function, provides strong evidence for the further development of a nanodiamond-MMP9 biosensor for metastasis site detection. More importantly, this work provides the foundation for use of nanodiamonds as a platform for stimuli-responsive biosensors and theranostic complexes that can be implemented across a wide range of biomedical applications.