Prediction of Nanoparticle Sizes for Arbitrary Methacrylates Using Artificial Neuronal Networks

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Novel Life Prediction Method of PMMA for Cultural Relics Protection Based on the BP Neural Network

Poly(methyl methacrylate) (PMMA) is widely used in the preservation and exhibition of cultural relics in museums. Accurately predicting its service life can help avoid many negative effects caused by PMMA aging. To study the change in the yellowing index of PMMA after aging in a UV light environment, an aging experiment was conducted. A prediction model for the service life of PMMA was established using nonlinear curve fitting and a back propagation (BP) neural network. By comparing the goodness of fit, simulation and modeling capabilities of the initial data, and the predictive ability for new data, it was found that the BP neural network prediction model outperformed the nonlinear curve fitting prediction model. In this study, the service life of newly produced PMMA samples was calculated as 7.83, 8.47, and 8.42 years, based on the yellowing index of retired PMMA as a benchmark and using the output data from the BP neural network prediction model. At this time, the performance and exhibition effect of the PMMA are poor, and the batch of PMMA needs to be updated.

Machine Learning Enabled Image Analysis of Time-Temperature Sensing Colloidal Arrays

Smart, responsive materials are required in various advanced applications ranging from anti-counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature. Here, an approach is demonstrated to simultaneously and independently measure the time and temperature solely based on the inherent material properties of complex colloidal crystal mixtures. An array of colloidal crystals, each featuring unique film formation kinetics, is fabricated. Combined with machine learning-enabled image analysis, the colloidal crystal arrays can autonomously record isothermal heating events - readout proceeds by acquiring photographs of the applied sensor using a standard smartphone camera. The concept shows how the progressing use of machine learning in materials science has the potential to allow non-classical forms of data acquisition and evaluation. This can provide novel insights into multiparameter systems and simplify applications of novel materials.

A Cyclodiaryliodonium NOX Inhibitor for the Treatment of Pancreatic Cancer via Enzyme-Activatable Targeted Delivery by Sulfated Glycosaminoglycan Derivatives

Pancreatic cancer renders a principal cause of cancer mortalities with a dismal prognosis, lacking sufficiently safe and effective therapeutics. Here, diversified cyclodiaryliodonium (CDAI) NADPH oxidase (NOX) inhibitors are rationally designed with tens of nanomolar optimal growth inhibition, and CD44-targeted delivery is implemented using synthesized sulfated glycosaminoglycan derivatives. The self-assembled nanoparticle-drug conjugate (NDC) enables hyaluronidase-activatable controlled release and facilitates cellular trafficking. NOX inhibition reprograms the metabolic phenotype by simultaneously impairing mitochondrial respiration and glycolysis. Moreover, the NDC selectively diminishes non-mitochondrial reactive oxygen species (ROS) but significantly elevates cytotoxic ROS through mitochondrial membrane depolarization. Transcriptomic profiling reveals perturbed p53, NF-κB, and GnRH signaling pathways interconnected with NOX inhibition. After being validated in patient-derived pancreatic cancer cells, the anticancer efficacy is further verified in xenograft mice bearing heterotopic and orthotopic pancreatic tumors, with extended survival and ameliorated systemic toxicity. It is envisaged that the translation of cyclodiaryliodonium inhibitors with an optimized molecular design can be expedited by enzyme-activatable targeted delivery with improved pharmacokinetic profiles and preserved efficacy.

Improvement of High-Throughput Experimentation Using Synthesis Robots by the Implementation of Tailor-Made Sensors

A small, low-cost, self-produced photometer is implemented into a synthesis robot and combined with a modified UV chamber to enable automated sampling and online characterization. In order to show the usability of the new approach, two different reversible addition–fragmentation chain transfer (RAFT) polymers were irradiated with UV light. Automated sampling and subsequent characterization revealed different reaction kinetics depending on polymer type. Thus, a long initiation time (20 min) is required for the end-group degradation of poly(ethylene glycol) ether methyl methacrylate (poly(PEGMEMA)), whereas poly(methyl methacrylate) (PMMA) is immediately converted. Lastly, all photometric samples are characterized via size-exclusion chromatography using UV and RI detectors to prove the results of the self-produced sensor and to investigate the molar mass shift during the reaction.