Computationally guided high-throughput design of self-assembling drug nanoparticles

Network pharmacology combined with affinity ultrafiltration to elucidate the potential compounds of Shaoyao Gancao Fuzi Decoction for the treatment of rheumatoid arthritis

ETHNOPHARMACOLOGICAL RELEVANCE

Shaoyao Gancao Fuzi Decoction (SGFD), has been employed for thousands of years in the treatment of rheumatoid arthritis (RA) with remarkable clinical efficacy. However, the material basis underlying the effectiveness of SGFD still remains unclear.

AIM OF THE REVIEW

This study aims to elucidate the material basis of SGFD through the application of network pharmacology and biological affinity ultrafiltration.

RESULTS

UPLC-Q-TOF-MS/MS was employed to characterize the components in SGFD, the identified 145 chemical components were mainly categorized into alkaloids, flavonoids, triterpenoids, and monoterpenoids according to the structures. Network pharmacology method was utilized to identify potential targets and signaling pathways of SGFD in the RA treatment, and the anti-inflammatory and anti-RA effects of SGFD were validated through in vivo and in vitro experiments. Moreover, as the significant node in the pharmacology network, TNF-α, a classical therapeutic target in RA, was subsequent employed to screen the interacting compounds in SGFD via affinity ultrafiltration screening method, 6 active molecules (i.e.,glycyrrhizic acid, paeoniflorin, formononetin, isoliquiritigenin, benzoyl mesaconitine, and glycyrrhetinic acid) were exhibited significant interactions. Finally, the significant anti-inflammatory and anti-TNF-α effects of these compounds were validated at the cellular level.

CONCLUSIONS

In conclusion, this study comprehensively elucidates the pharmacodynamic material basis of SGFD, offering a practical reference model for the systematic investigation of traditional Chinese medicine formulas.

The landscape of small-molecule prodrugs

Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs encompass a wide range of different chemical structures, therapeutic indications and properties. Here we provide the first holistic analysis of the current landscape of approved prodrugs using cheminformatics and data science approaches to reveal trends in prodrug development. We highlight rationales that underlie prodrug design, their indications, mechanisms of API release, the chemistry of promoieties added to APIs to form prodrugs and the market impact of prodrugs. On the basis of this analysis, we discuss strengths and limitations of current prodrug approaches and suggest areas for future development.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Combinatorial discovery of antibacterials via a feature-fusion based machine learning workflow

The discovery of new antibacterials within the vast chemical space is crucial in combating drug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). However, the traditional approach of screening the entire chemical library in an ergodic manner can be laborious and time-consuming. Machine learning-assisted screening of antibacterials alleviates the exploration effort but suffers from the lack of reliable and related datasets. To address these challenges, we devised a combinatorial library comprising over 110 000 candidates based on the Ugi reaction. A focused library was subsequently generated through uniform sampling of the entire library to narrow down the preliminary screening scale. A novel feature-fusion architecture called the latent space constraint neural network was developed which incorporated both fingerprint and physicochemical molecular descriptors to predict the antibacterial properties. This integration allowed the model to leverage the complementary information provided by these descriptors and improve the accuracy of predictions. Three lead compounds that demonstrated excellent efficacy against MRSA while alleviating drug resistance were identified. This workflow highlights the integration of machine learning with the combinatorial chemical library to expedite high-quality data collection and extensive data mining for antibacterial screening.

AI in cellular engineering and reprogramming

During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Strategies for non-viral vectors targeting organs beyond the liver

In recent years, nanoparticles have evolved to a clinical modality to deliver diverse nucleic acids. Rising interest in nanomedicines comes from proven safety and efficacy profiles established by continuous efforts to optimize physicochemical properties and endosomal escape. However, despite their transformative impact on the pharmaceutical industry, the clinical use of non-viral nucleic acid delivery is limited to hepatic diseases and vaccines due to liver accumulation. Overcoming liver tropism of nanoparticles is vital to meet clinical needs in other organs. Understanding the anatomical structure and physiological features of various organs would help to identify potential strategies for fine-tuning nanoparticle characteristics. In this Review, we discuss the source of liver tropism of non-viral vectors, present a brief overview of biological structure, processes and barriers in select organs, highlight approaches available to reach non-liver targets, and discuss techniques to accelerate the discovery of non-hepatic therapies.

New concepts drive the development of delivery tools for sustainable treatment of diabetic complications

Diabetic complications, especially diabetic retinopathy, diabetic nephropathy and painful diabetic neuropathy, account for a large portion of patients with diabetes and display rising global prevalence. They are the leading causes of blindness, kidney failure and hypersensitivity to pain caused by diabetes. Current approved therapeutics against the diabetic complications are few and exhibit limited efficacy. The enhanced cell-specificity, stability, biocompatibility, and loading capacity of drugs are essential for the mitigation of diabetic complications. In the article, we have critically discussed the recent studies over the past two years in material sciences and biochemistry. The insightful concepts in these studies drive the development of novel nanoparticles and mesenchymal stem cells-derived extracellular vesicles to meet the need for treatment of diabetic complications. Their underlying biochemical principles, advantages and limitations have been in-depth analyzed. The nanoparticles discussed in the article include double-headed nanodelivery system, nanozyme, ESC-HCM-B system, soft polymer nanostars, tetrahedral DNA nanostructures and hydrogels. They ameliorate the diabetic complication through attenuation of inflammation, apoptosis and restoration of metabolic homeostasis. Moreover, mesenchymal stem cell-derived extracellular vesicles efficiently deliver therapeutic proteins to the retinal cells to suppress the angiogenesis, inflammation, apoptosis and oxidative stress to reverse diabetic retinopathy. Collectively, we provide a critical discussion on the concept, mechanism and therapeutic applicability of new delivery tools to treat these three devastating diabetic complications.

Applying artificial intelligence and computational modeling to nanomedicine

Achieving specific and targeted delivery of nanomedicines to diseased tissues is a major challenge. This is because the process of designing, formulating, testing, and selecting a nanoparticle delivery vehicle for a specific disease target is governed by complex multivariate interactions. Computational modeling and artificial intelligence are well-suited for analyzing and modeling large multivariate datasets in short periods of time. Computational approaches can be applied to help design nanomedicine formulations, interpret nanoparticle-biological interactions, and create models from high-throughput screening techniques to improve the selection of the ideal nanoparticle carrier. In the future, many steps in the nanomedicine development process will be done computationally, reducing the number of experiments and time needed to select the ideal nanomedicine formulation.

Step-By-Step Standardization of the Bottom-Up Semi-Automated Nanocrystallization of Pharmaceuticals: A Quality By Design and Design of Experiments Joint Approach

Nanosized drug crystals have been reported with enhanced apparent solubility, bioavailability, and therapeutic efficacy compared to microcrystal materials, which are not suitable for parenteral administration. However, nanocrystal design and development by bottom-up approaches are challenging, especially considering the non-standardized process parameters in the injection step. This work aims to present a systematic step-by-step approach through Quality-by-Design (QbD) and Design of Experiments (DoE) for synthesizing drug nanocrystals by a semi-automated nanoprecipitation method. Curcumin is used as a drug model due to its well-known poor water solubility (0.6 µg mL-1 , 25 °C). Formal and informal risk assessment tools allow identifying the critical factors. A fractional factorial 24-1 screening design evaluates their impact on the average size and polydispersity of nanocrystals. The optimization of significant factors is done by a Central Composite Design. This response surface methodology supports the rational design of the nanocrystals, identifying and exploring the design space. The proposed joint approach leads to a reproducible, robust, and stable nanocrystalline preparation of 316 nm with a PdI of 0.217 in compliance with the quality profile. An orthogonal approach for particle size and polydispersity characterization allows discarding the formation of aggregates. Overall, the synergy between advanced data analysis and semi-automated standardized nanocrystallization of drugs is highlighted.

Self-Assembled Nanotubes Based on Chiral H8-BINOL Modified with 1,2,3-Triazole to Recognize Bi3+ Efficiently by ICT Mechanism

A novel fluorescent "off" probe R-β-D-1 containing a 1,2,3-triazole moiety was obtained by the Click reaction with azidoglucose using H8-BINOL as a substrate, and the structure was characterized by 1H NMR and 13C NMR and ESI-MS analysis. The fluorescence properties of R-β-D-1 in methanol were investigated, and it was found that R-β-D-1 could be selectively fluorescently quenched by Bi3+ in the recognition of 19 metal ions and basic cations. The recognition process of Bi3+ by R-β-D-1 was also investigated by fluorescence spectroscopy, SEM, AFM, etc. The complex pattern of R-β-D-1 with Bi3+ was determined by Job's curve as 1 + 1, and the binding constant Ka of R-β-D-1 and Bi3+ was valued by the Benesi-Hildebrand equation as 1.01 × 104 M-1, indicating that the binding force of R-β-D-1 and Bi3+ was medium. The lowest detection limit (LOD) of the self-assembled H8-BINOL derivative for Bi3+ was up to 0.065 µM. The mechanism for the recognition of Bi3+ by the sensor R-β-D-1 may be the intramolecular charge transfer effect (ICT), which was attributed to the fact that the N-3 of the triazole readily serves as an electron acceptor while the incorporation of Bi3+ serves as an electron donor, and the two readily undergo coordination leading to the quenching of fluorescence. The recognition mechanism and recognition site could be verified by DFT calculation and CDD (Charge Density Difference).

Transferosomes as a transdermal drug delivery system: Dermal kinetics and recent developments

The development of innovative approaches to deliver medications has been growing now for the last few decades and generates a growing interest in the dermatopharmaceutical field. Transdermal drug delivery in particular, remains an attractive alternative route for many therapeutics. However, due to the limitations posed by the barrier properties of the stratum corneum, the delivery of many pharmaceutical dosage forms remains a challenge. Most successful therapies using the transdermal route have been ones containing smaller lipophilic molecules with molecular weights of a few hundred Daltons. To overcome these limitations of size and lipophilicity of the drugs, transferosomes have emerged as a successful tool for transdermal delivery of a variety of therapeutics including hydrophilic actives, larger molecules, peptides, proteins, and nucleic acids. Transferosomes exhibit a flexible structure and higher surface hydrophilicity which both play a critical role in the transport of drugs and other solutes using hydration gradients as a driving force to deliver the molecules into and across the skin. This results in enhanced overall permeation as well as controlled release of the drug in the skin layers. Additionally, the physical-chemical properties of the transferosomes provide increased stability by preventing degradation of the actives by oxidation, light, and temperature. Here, we present the history of transferosomes from solid lipid nanoparticles and liposomes, their physical-chemical properties, dermal kinetics, and their recent advances as marketed dosage forms. This article is categorized under: Biology-Inspired Nanomaterials > Lipid-Based Structures Therapeutic Approaches and Drug Discovery > Emerging Technologies.

The Potential of Congo Red Supplied Aggregates of Multitargeted Tyrosine Kinase Inhibitor (Sorafenib, BAY-43-9006) in Enhancing Therapeutic Impact on Bladder Cancer

Bladder cancer is a common malignancy associated with high recurrence rates and potential progression to invasive forms. Sorafenib, a multi-targeted tyrosine kinase inhibitor, has shown promise in anti-cancer therapy, but its cytotoxicity to normal cells and aggregation in solution limits its clinical application. To address these challenges, we investigated the formation of supramolecular aggregates of sorafenib with Congo red (CR), a bis-azo dye known for its supramolecular interaction. We analyzed different mole ratios of CR-sorafenib aggregates and evaluated their effects on bladder cancer cells of varying levels of malignancy. In addition, we also evaluated the effect of the test compounds on normal uroepithelial cells. Our results demonstrated that sorafenib inhibits the proliferation of bladder cancer cells and induces apoptosis in a dose-dependent manner. However, high concentrations of sorafenib also showed cytotoxicity to normal uroepithelial cells. In contrast, the CR-BAY aggregates exhibited reduced cytotoxicity to normal cells while maintaining anti-cancer activity. The aggregates inhibited cancer cell migration and invasion, suggesting their potential for metastasis prevention. Dynamic light scattering and UV-VIS measurements confirmed the formation of stable co-aggregates with distinctive spectral properties. These CR-sorafenib aggregates may provide a promising approach to targeted therapy with reduced cytotoxicity and improved stability for drug delivery in bladder cancer treatment. This work shows that the drug-excipient aggregates proposed and described so far, as Congo red-sorafenib, can be a real step forward in anti-cancer therapies.

Fragment-based drug nanoaggregation reveals drivers of self-assembly

Drug nanoaggregates are particles that can deleteriously cause false positive results during drug screening efforts, but alternatively, they may be used to improve pharmacokinetics when developed for drug delivery purposes. The structural features of molecules that drive nanoaggregate formation remain elusive, however, and the prediction of intracellular aggregation and rational design of nanoaggregate-based carriers are still challenging. We investigate nanoaggregate self-assembly mechanisms using small molecule fragments to identify the critical molecular forces that contribute to self-assembly. We find that aromatic groups and hydrogen bond acceptors/donors are essential for nanoaggregate formation, suggesting that both π-π stacking and hydrogen bonding are drivers of nanoaggregation. We apply structure-assembly-relationship analysis to the drug sorafenib and discover that nanoaggregate formation can be predicted entirely using drug fragment substructures. We also find that drug nanoaggregates are stabilized in an amorphous core-shell structure. These findings demonstrate that rational design can address intracellular aggregation and pharmacologic/delivery challenges in conventional and fragment-based drug development processes.

Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean absolute errors of 1.652 and 0.0105 for modeling the size and PDI, respectively. Under conditions where the temperature was set at approximately 60 °C, our EL model predicted a minimum particle size of 116.53 nm for composition (A) with a sonication time of approximately 30 min. Similarly, for composition (B), the model predicted a minimum particle size of 129.97 nm with sonication times of approximately 30 or 55 min. In most instances, a PDI of less than 0.2 was achieved. These results highlight the significant impact of optimizing independent factors on the characteristics of liposomal nanoparticles and demonstrate the potential of EL as a decision support system for identifying the best liposomal formulation. We recommend further studies to explore the effects of other independent factors, such as lipid composition and surfactants, on liposomal nanoparticle characteristics.

STING agonist-boosted mRNA immunization via intelligent design of nanovaccines for enhancing cancer immunotherapy

Messenger RNA (mRNA) vaccine is revolutionizing the methodology of immunization in cancer. However, mRNA immunization is drastically limited by multistage biological barriers including poor lymphatic transport, rapid clearance, catalytic hydrolysis, insufficient cellular entry and endosome entrapment. Herein, we design a mRNA nanovaccine based on intelligent design to overcome these obstacles. Highly efficient nanovaccines are carried out with machine learning techniques from datasets of various nanocarriers, ensuring successful delivery of mRNA antigen and cyclic guanosine monophosphate-adenosine monophosphate (cGAMP) to targets. It activates stimulator of interferon genes (STING), promotes mRNA-encoded antigen presentation and boosts antitumour immunity in vivo, thus inhibiting tumour growth and ensuring long-term survival of tumour-bearing mice. This work provides a feasible and safe strategy to facilitate STING agonist-synergized mRNA immunization, with great translational potential for enhancing cancer immunotherapy.

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels

Marginalized graph kernels have shown competitive performance in molecular machine learning tasks but currently lack measures of interpretability, which are important to improve trust in the models, detect biases, and inform molecular optimization campaigns. We here conceive and implement two interpretability measures for Gaussian process regression using a marginalized graph kernel (GPR-MGK) to quantify (1) the contribution of specific training data to the prediction and (2) the contribution of specific nodes of the graph to the prediction. We demonstrate the applicability of these interpretability measures for molecular property prediction. We compare GPR-MGK to graph neural networks on four logic and two real-world toxicology data sets and find that the atomic attribution of GPR-MGK generally outperforms the atomic attribution of graph neural networks. We also perform a detailed molecular attribution analysis using the FreeSolv data set, showing how molecules in the training set influence machine learning predictions and why Morgan fingerprints perform poorly on this data set. This is the first systematic examination of the interpretability of GPR-MGK and thereby is an important step in the further maturation of marginalized graph kernel methods for interpretable molecular predictions.

Prediction of cancer nanomedicines self-assembled from meta-synergistic drug pairs

Combination therapy is widely used in cancer medicine due to the benefits of drug synergy and the reduction of acquired resistance. To minimize emergent toxicities, nanomedicines containing drug combinations are being developed, and they have shown encouraging results. However, developing multi-drug loaded nanoparticles is highly complex and lacks predictability. Previously, it was shown that single drugs can self-assemble with near-infrared dye, IR783, to form cancer-targeted nanoparticles. A structure-based predictive model showed that only 4% of the drug space self-assembles with IR783. Here, we mapped the self-assembly outcomes of 77 small molecule drugs and drug pairs with IR783. We found that the small molecule drug space can be divided into five types, and type-1 drugs self-assemble with three out of four possible drug types that do not form stable nanoparticles. To predict the self-assembly outcome of any drug pair, we developed a machine learning model based on decision trees, which was trained and tested with F1-scores of 89.3% and 87.2%, respectively. We used literature text mining to capture drug pairs with biological synergy together with synergistic chemical self-assembly and generated a database with 1985 drug pairs for 70 cancers. We developed an online search tool to identify cancer-specific, meta-synergistic drug pairs (both chemical and biological synergism) and validated three different pairs in vitro. Lastly, we discovered a novel meta-synergistic pair, bortezomib-cabozantinib, which formed stable nanoparticles with improved biodistribution, efficacy, and reduced toxicity, even over single drugs, in an in vivo model of head and neck cancer.

Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications

Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

Interdisciplinary advances reshape the delivery tools for effective NASH treatment

BACKGROUND

Nonalcoholic steatohepatitis (NASH), a severe systemic and inflammatory subtype of nonalcoholic fatty liver disease, eventually develops into cirrhosis and hepatocellular carcinoma with few options for effective treatment. Currently potent small molecules identified in preclinical studies are confronted with adverse effects and long-term ineffectiveness in clinical trials. Nevertheless, highly specific delivery tools designed from interdisciplinary concepts may address the significant challenges by either effectively increasing the concentrations of drugs in target cell types, or selectively manipulating the gene expression in liver to resolve NASH.

SCOPE OF REVIEW

We focus on dissecting the detailed principles of the latest interdisciplinary advances and concepts that direct the design of future delivery tools to enhance the efficacy. Recent advances have indicated that cell and organelle-specific vehicles, non-coding RNA research (e.g. saRNA, hybrid miRNA) improve the specificity, while small extracellular vesicles and coacervates increase the cellular uptake of therapeutics. Moreover, strategies based on interdisciplinary advances drastically elevate drug loading capacity and delivery efficiency and ameliorate NASH and other liver diseases.

MAJOR CONCLUSIONS

The latest concepts and advances in chemistry, biochemistry and machine learning technology provide the framework and strategies for the design of more effective tools to treat NASH, other pivotal liver diseases and metabolic disorders.

Synthetic Ionizable Colloidal Drug Aggregates Enable Endosomal Disruption

Colloidal drug aggregates enable the design of drug-rich nanoparticles; however, the efficacy of stabilized colloidal drug aggregates is limited by entrapment in the endo-lysosomal pathway. Although ionizable drugs are used to elicit lysosomal escape, this approach is hindered by toxicity associated with phospholipidosis. It is hypothesized that tuning the pK_a of the drug would enable endosomal disruption while avoiding phospholipidosis and minimizing toxicity. To test this idea, 12 analogs of the nonionizable colloidal drug fulvestrant are synthesized with ionizable groups to enable pH-dependent endosomal disruption while maintaining bioactivity. Lipid-stabilized fulvestrant analog colloids are endocytosed by cancer cells, and the pK_a of these ionizable colloids influenced the mechanism of endosomal and lysosomal disruption. Four fulvestrant analogs-those with pK_a values between 5.1 and 5.7-disrupted endo-lysosomes without measurable phospholipidosis. Thus, by manipulating the pK_a of colloid-forming drugs, a tunable and generalizable strategy for endosomal disruption is established.

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

A Supramolecular Assembly Strategy for Hydrophilic Drug Delivery towards Synergistic Cancer Treatment

To improve the drug loading, tumor targeting, and delivery simplicity of hydrophilic drugs, we propose a supramolecular assembly strategy that potentially benefits a wide range of hydrophilic drug delivery. Firstly, we choose a hydrophilic drug (tirapazamine) as a model drug to directly co-assemble with chlorin e6 (Ce6) at different molar ratios, and systematically evaluate the resultant Ce6-tirapazamine nanoparticles (CT NPs) in aspects of size distribution, polydispersity, morphology, optical properties and molecular dynamics simulation. Based on the assembling facts between Ce6 and tirapazamine, we summarize a plausible rule of the supramolecular assembly for hydrophilic drugs. To validate our findings, more drugs with increasing hydrophilicity, such as temozolomide, gemcitabine hydrochloride and 5-azacytidine, successfully co-assemble with Ce6 into nanostructures by following similar assembling behaviors, demonstrating that our assembling rule may guide a wide range of hydrophilic drug delivery. Next, the combination of Ce6 and tirapazamine was chosen as the representative to investigate the anti-tumor activities of the supramolecular assemblies. CT NPs showed synergistic anti-tumor efficacy, increased tumor accumulation and significant tumor progression and metastasis inhibition in tumor-bearing mice. We anticipate that the supramolecular assembly mechanism will provide broad guidance for developing easy-to-make but functional nanomedicines. STATEMENT OF SIGNIFICANCE: Although thousands of nanomedicines have been developed, only a few have been approved for clinical use. The manufacturing complexity significantly hinders the "bench-to-bed" translation of nanomedicines. Hence, we need to rethink how to conduct research on translational nanomedicines by avoiding more and more complex chemistry and complicated nanostructures. Here, we summarize a plausible rule according to multiple supramolecular assembly pairs and propose a supramolecular assembly strategy that can improve the drug loading, tumor targeting, and manufacturing simplicity of nanomedicine for hydrophilic drugs. The supramolecular assembly strategy would guide a broader range of drug delivery to provide a new paradigm for developing easy-to-make but multifunctional nanoformulations for synergistic cancer treatment.

Spotlight on Genetic Kidney Diseases: A Call for Drug Delivery and Nanomedicine Solutions

Nanoparticles as drug delivery carriers have benefited diseases, including cancer, since the 1990s, and more recently, their promise to quickly and efficiently be mobilized to fight against global diseases such as in the COVID-19 pandemic have been proven. Despite these success stories, there are limited nanomedicine efforts for chronic kidney diseases (CKDs), which affect 844 million people worldwide and can be linked to a variety of genetic kidney diseases. In this Perspective, we provide a brief overview of the clinical status of genetic kidney diseases, background on kidney physiology and a summary of nanoparticle design that enable kidney access and targeting, and emerging technological strategies that can be applied for genetic kidney diseases, including rare and congenital kidney diseases. Finally, we conclude by discussing gaps in knowledge remaining in both genetic kidney diseases and kidney nanomedicine and collective efforts that are needed to bring together stakeholders from diverse expertise and industries to enable the development of the most relevant drug delivery strategies that can make an impact in the clinic.

Towards machine learning for hydrogel drug delivery systems

Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the process. We highlight recent advances and strategies for data collection and ML, and we discuss the potential for and barriers to the broader use of ML for hydrogel drug delivery systems.

Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery

Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.

Biomimetic mineralization based on self-assembling peptides

Biomimetic science has attracted great interest in the fields of chemistry, biology, materials science, and energy. Biomimetic mineralization is the process of synthesizing inorganic minerals under the control of organic molecules or biomolecules under mild conditions. Peptides are the motifs that constitute proteins, and can self-assemble into various hierarchical structures and show a high affinity for inorganic substances. Therefore, peptides can be used as building blocks for the synthesis of functional biomimetic materials. With the participation of peptides, the morphology, size, and composition of mineralized materials can be controlled precisely. Peptides not only provide well-defined templates for the nucleation and growth of inorganic nanomaterials but also have the potential to confer inorganic nanomaterials with high catalytic efficiency, selectivity, and biotherapeutic functions. In this review, we systematically summarize research progress in the formation mechanism, nanostructural manipulation, and applications of peptide-templated mineralized materials. These can further inspire researchers to design structurally complex and functionalized biomimetic materials with great promising applications.

Multidisciplinary Advances Address the Challenges in Developing Drugs against Transient Receptor Potential Channels to Treat Metabolic Disorders

Transient receptor potential (TRP) channels are cation channels that regulate key physiological and pathological processes in response to a broad range of stimuli. Moreover, they systemically regulate the release of hormones, metabolic homeostasis, and complications of diabetes, which positions them as promising therapeutic targets to combat metabolic disorders. Nevertheless, there are significant challenges in the design of TRP ligands with high potency and durability. Herein we summarize the four challenges as hydrophobicity, selectivity, mono-target therapy, and interspecies discrepancy. We present 1134 TRP ligands with diversified modes of TRP-ligand interaction and provide a detailed discussion of the latest strategies, especially cryogenic electron microscopy (cryo-EM) and computational methods. We propose solutions to address the challenges with a critical analysis of advances in membrane partitioning, polypharmacology, biased agonism, and biochemical screening of transcriptional modulators. They are fueled by the breakthrough from cryo-EM, chemoinformatics and bioinformatics. The discussion is aimed to shed new light on designing next-generation drugs to treat obesity, diabetes and its complications, with optimal hydrophobicity, higher mode selectivity, multi-targeting and consistent activities between human and rodents.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

Cu2O nanoparticles synthesized by green and chemical routes, and evaluation of their antibacterial and antifungal effect on functionalized textiles

The potential for the application of metal-containing nanomaterials at the nanoscale promotes the opportunity to search for new methods for their elaboration, with special attention to those sustainable methods. In response to these challenges, we have investigated a new method for green synthesis of cuprous oxide nanoparticles (Cu₂O NPs) using Myrciaria dubia juice as an organic reductant and, comparing it with chemical synthesis, evaluating in both cases the influence of the volume of the organic (juice) and chemical (ascorbic acid) reductants, for which a large number of techniques such as spectrophotometry, EDX spectrometry, TEM, SEM, DLS, FTIR spectroscopy have been used. Likewise, the nanomaterial with better morphological characteristics, stability, and size homogeneity has been applied in the functionalization of textiles by means of in situ and post-synthesis impregnation methods. The success of the synthesis process has been demonstrated by the antimicrobial activity (bacteria and fungi) of textiles impregnated with Cu₂O NPs.

Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences

Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6-9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.

Colonic drug delivery: Formulating the next generation of colon-targeted therapeutics

Colonic drug delivery can facilitate access to unique therapeutic targets and has the potential to enhance drug bioavailability whilst reducing off-target effects. Delivering drugs to the colon requires considered formulation development, as both oral and rectal dosage forms can encounter challenges if the colon's distinct physiological environment is not appreciated. As the therapeutic opportunities surrounding colonic drug delivery multiply, the success of novel pharmaceuticals lies in their design. This review provides a modern insight into the key parameters determining the effective design and development of colon-targeted medicines. Influential physiological features governing the release, dissolution, stability, and absorption of drugs in the colon are first discussed, followed by an overview of the most reliable colon-targeted formulation strategies. Finally, the most appropriate in vitro, in vivo, and in silico preclinical investigations are presented, with the goal of inspiring strategic development of new colon-targeted therapeutics.

Nanotechnology-Driven Delivery Systems in Inoculation Therapies

Nanotechnology and genomics are the newest allies of inoculation design. In recent years, nucleic acids have been targeted as sources of therapeutics to stimulate immune responses, to both fight disease and create memory to trigger further responses to threat. A myriad of promising findings in cancer research and virology has been reported in the current literature. Nanosystems are demonstrating their capabilities as efficient carriers, improving the efficacy of drug delivery, including nucleic acids as therapeutics, at focal sites, in living systems. This chapter approaches major elements involved in the successful use of nanotechnology as delivery platforms to optimise the efficacy of nucleic acids-driven therapeutics, particularly mRNA vectors as coding engines for targeted viral proteins. Latest findings in nanotechnological design are highlighted, key discoveries associated with the success of nanodelivery platforms are presented, and key characteristics of nanodelivery systems in nucleic acids-based vaccine technology are discussed, to illustrate their distinct advantages and disadvantages.

Molecular features and predictive models identify the most lethal subtype and a therapeutic target for osteosarcoma

Background

Osteosarcoma is the most common primary malignant bone tumor. The existing treatment regimens remained essentially unchanged over the past 30 years; hence the prognosis has plateaued at a poor level. Precise and personalized therapy is yet to be exploited.

Methods

One discovery cohort (n=98) and two validation cohorts (n=53 & n=48) were collected from public data sources. We performed a non-negative matrix factorization (NMF) method on the discovery cohort to stratify osteosarcoma. Survival analysis and transcriptomic profiling characterized each subtype. Then, a drug target was screened based on subtypes' features and hazard ratios. We also used specific siRNAs and added a cholesterol pathway inhibitor to osteosarcoma cell lines (U2OS and Saos-2) to verify the target. Moreover, PermFIT and ProMS, two support vector machine (SVM) tools, and the least absolute shrinkage and selection operator (LASSO) method, were employed to establish predictive models.

Results

We herein divided osteosarcoma patients into four subtypes (S-I ~ S-IV). Patients of S- I were found probable to live longer. S-II was characterized by the highest immune infiltration. Cancer cells proliferated most in S-III. Notably, S-IV held the most unfavorable outcome and active cholesterol metabolism. SQLE, a rate-limiting enzyme for cholesterol biosynthesis, was identified as a potential drug target for S-IV patients. This finding was further validated in two external independent osteosarcoma cohorts. The function of SQLE to promote proliferation and migration was confirmed by cell phenotypic assays after the specific gene knockdown or addition of terbinafine, an inhibitor of SQLE. We further employed two machine learning tools based on SVM algorithms to develop a subtype diagnostic model and used the LASSO method to establish a 4-gene model for predicting prognosis. These two models were also verified in a validation cohort.

Conclusion

The molecular classification enhanced our understanding of osteosarcoma; the novel predicting models served as robust prognostic biomarkers; the therapeutic target SQLE opened a new way for treatment. Our results served as valuable hints for future biological studies and clinical trials of osteosarcoma.

The past, present, and future of chemotherapy with a focus on individualization of drug dosing

While there have been rapid advances in developing new and more targeted drugs to treat cancer, much less progress has been made in individualizing dosing. Even though the introduction of immunotherapies such as CAR T-cells and checkpoint inhibitors, as well as personalized therapies that target specific mutations, have transformed clinical treatment of cancers, chemotherapy remains a mainstay in oncology. Chemotherapies are typically dosed on either a body surface area (BSA) or weight basis, which fails to account for pharmacokinetic differences between patients. Drug absorption, distribution, metabolism, and excretion rates can vary between patients, resulting in considerable differences in exposure to the active drugs. These differences result in suboptimal dosing, which can reduce efficacy and increase side-effects. Therapeutic drug monitoring (TDM), genotype guided dosing, and chronomodulation have been developed to address this challenge; however, despite improving clinical outcomes, they are rarely implemented in clinical practice for chemotherapies. Thus, there is a need to develop interventions that allow for individualized drug dosing of chemotherapies, which can help maximize the number of patients that reach the most efficacious level of drug in the blood while mitigating the risks of underdosing or overdosing. In this review, we discuss the history of the development of chemotherapies, their mechanisms of action and how they are dosed. We discuss substantial intraindividual and interindividual variability in chemotherapy pharmacokinetics. We then propose potential engineering solutions that could enable individualized dosing of chemotherapies, such as closed-loop drug delivery systems and bioresponsive biomaterials.

Automated discovery of nanomaterials via drug aggregation induced emission

Nanoformulations of small molecule drugs are essential to effectively deliver them and treat a wide range of diseases. They are normally complex to develop, lack predictability, and exhibit low drug loading. Recently, nanoparticles made via co-assembly of hydrophobic drugs and organic dyes, exhibited drug-loading of up to 90% with high predictability from the drug structure. However, these particles have relatively short stability and can formulate only a small fraction of the drug space. Here, we developed an automated workflow to synthesize and select novel dye stabilizers, based on their ability to inhibit drug aggregation-induced emission (AIE). We first screened and identified 10 drugs with previously unknown strong AIE activity and exploited this trait to automatically synthesize and select a new ultra-stabilizer named R595. Interestingly, it shares several synthetic similarities and advantages with polydopamine. We found that R595 is superior to myriad types of excipients and solubilizers such as cyclodextrins, poloxamers, albumin, and previously published organic dyes, in both long-term stability and drug compatibility. We investigated the biodistribution, pharmacokinetics, safety and efficacy of the AIEgenic MEK inhibitor trametinib-R595 nanoparticles in vitro and in vivo and demonstrated that they are non-toxic and effective in KRAS driven colon and lung cancer models.

Microfluidic Nanoparticles for Drug Delivery

Nanoparticles (NPs) have attracted tremendous interest in drug delivery in the past decades. Microfluidics offers a promising strategy for making NPs for drug delivery due to its capability in precisely controlling NP properties. The recent success of mRNA vaccines using microfluidics represents a big milestone for microfluidic NPs for pharmaceutical applications, and its rapid scaling up demonstrates the feasibility of using microfluidics for industrial-scale manufacturing. This article provides a critical review of recent progress in microfluidic NPs for drug delivery. First, the synthesis of organic NPs using microfluidics focusing on typical microfluidic methods and their applications in making popular and clinically relevant NPs, such as liposomes, lipid NPs, and polymer NPs, as well as their synthesis mechanisms are summarized. Then, the microfluidic synthesis of several representative inorganic NPs (e.g., silica, metal, metal oxide, and quantum dots), and hybrid NPs is discussed. Lastly, the applications of microfluidic NPs for various drug delivery applications are presented.

Quantitatively Determining Surface-Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning

Learning microscopic properties of a material from its macroscopic measurables is a grand and challenging goal in physical science. Conventional wisdom is to first identify material structures exploiting characterization tools, such as spectroscopy, and then to infer properties of interest, often with assistance of theory and simulations. This indirect approach has limitations due to the accumulation of errors from retrieving structures from spectral signals and the lack of quantitative structure-property relationship. A new pathway directly from spectral signals to microscopic properties is highly desirable, as it would offer valuable guidance toward materials evaluation and design via spectroscopic measurements. Herein, we exploit machine-learned vibrational spectroscopy to establish quantitative spectrum-property relationships. Key interaction properties of substrate-adsorbate systems, including adsorption energy and charge transfer, are quantitatively determined directly from Infrared and Raman spectroscopic signals of the adsorbates. The machine-learned spectrum-property relationships are presented as mathematical formulas, which are physically interpretable and therefore transferrable to a series of metal/alloy surfaces. The demonstrated ability of quantitative determination of hard-to-measure microscopic properties using machine-learned spectroscopy will significantly broaden the applicability of conventional spectroscopic techniques for materials design and high throughput screening under operando conditions.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Evaluation guidelines for machine learning tools in the chemical sciences

Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generation, improvement and/or ordering of research hypotheses. Despite the overarching applicability of ML workflows, one usually finds diverse evaluation study designs. The current heterogeneity in evaluation techniques and metrics leads to difficulty in (or the impossibility of) comparing and assessing the relevance of new algorithms. Ultimately, this may delay the digitalization of chemistry at scale and confuse method developers, experimentalists, reviewers and journal editors. In this Perspective, we critically discuss a set of method development and evaluation guidelines for different types of ML-based publications, emphasizing supervised learning. We provide a diverse collection of examples from various authors and disciplines in chemistry. While taking into account varying accessibility across research groups, our recommendations focus on reporting completeness and standardizing comparisons between tools. We aim to further contribute to improved ML transparency and credibility by suggesting a checklist of retro-/prospective tests and dissecting their importance. We envisage that the wide adoption and continuous update of best practices will encourage an informed use of ML on real-world problems related to the chemical sciences.

Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.

Merging data curation and machine learning to improve nanomedicines

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.

Label-Free and In Situ Identification of Cells via Combinational Machine Learning Models

Cell identification and counting in living and coculture systems are crucial in cell interaction studies, but current methods primarily rely on complicated and time-consuming staining techniques. Here, a label-free method to precisely recognize, identify, and instantly count cells in situ in coculture systems via combinational machine learning models s presented. A convolutional neural network (CNN) model is first used to generate virtual images of cell nuclei based on unlabeled phase-contrast images. Coordinates of all the cells are then returned according to the virtual nucleus images using two clustering algorithms. Finally, phase-contrast images of single cells are cropped based on the coordinates and sent into another CNN model for cell-type identification. This combinational approach is highly automatic and efficient, which requires few to no manual annotations of images in the training phase. It shows practical performance in different cell culture conditions including cell ratios, densities, and substrate materials, having great potential in real-time cell tracking and analyzing.

Research progress on self-assembled nanodrug delivery systems

In recent years, nanodrug delivery systems have attracted increasing attention due to their advantages, such as the high drug loading, low toxicity and side effects, improved bioavailability, long half-life, well...

Algorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Sampling

Here, we report a biomarker-free detection of various biological targets through a programmed machine learning algorithm and an automated computational selection process termed algorithmically guided optical nanosensor selector (AGONS). The optical data processed/used by algorithms are obtained through a nanosensor array selected from a library of nanosensors through AGONS. The nanosensors are assembled using two-dimensional nanoparticles (2D-nps) and fluorescently labeled single-stranded DNAs (F-ssDNAs) with random sequences. Both 2D-np and F-ssDNA components are cost-efficient and easy to synthesize, allowing for scaled-up data collection essential for machine learning modeling. The nanosensor library was subjected to various target groups, including proteins, breast cancer cells, and lethal-7 (let-7) miRNA mimics. We have demonstrated that AGONS could select the most essential nanosensors while achieving 100% predictive accuracy in all cases. With this approach, we demonstrate that machine learning can guide the design of nanosensor arrays with greater predictive accuracy while minimizing manpower, material cost, computational resources, instrumentation usage, and time. The biomarker-free detection attribute makes this approach readily available for biological targets without any detectable biomarker. We believe that AGONS can guide optical nanosensor array setups, opening broader opportunities through a biomarker-free detection approach for most challenging biological targets.

Connected healthcare: Improving patient care using digital health technologies

Now more than ever, traditional healthcare models are being overhauled with digital technologies of Healthcare 4.0 increasingly adopted. Worldwide, digital devices are improving every stage of the patient care pathway. For one, sensors are being used to monitor patient metrics 24/7, permitting swift diagnosis and interventions. At the treatment stage, 3D printers are under investigation for the concept of personalised medicine by allowing patients access to on-demand, customisable therapeutics. Robots are also being explored for treatment, by empowering precision surgery, rehabilitation, or targeted drug delivery. Within medical logistics, drones are being leveraged to deliver critical treatments to remote areas, collect samples, and even provide emergency aid. To enable seamless integration within healthcare, the Internet of Things technology is being exploited to form closed-loop systems that remotely communicate with one another. This review outlines the most promising healthcare technologies and devices, their strengths, drawbacks, and opportunities for clinical adoption.

High-throughput screening of nanoparticles in drug delivery

The use of pharmacologically active compounds to manage and treat diseases is of utmost relevance in clinical practice. It is well recognized that spatial-temporal control over the delivery of these biomolecules will greatly impact their pharmacokinetic profile and ultimately their therapeutic effect. Nanoparticles (NPs) prepared from different materials have been tested successfully in the clinic for the delivery of several biomolecules including non-coding RNAs (siRNA and miRNA) and mRNAs. Indeed, the recent success of mRNA vaccines is in part due to progress in the delivery systems (NP based) that have been developed for many years. In most cases, the identification of the best formulation was done by testing a small number of novel formulations or by modification of pre-existing ones. Unfortunately, this is a low throughput and time-consuming process that hinders the identification of formulations with the highest potential. Alternatively, high-throughput combinatorial design of NP libraries may allow the rapid identification of formulations with the required release and cell/tissue targeting profile for a given application. Combinatorial approaches offer several advantages over conventional methods since they allow the incorporation of multiple components with varied chemical properties into materials, such as polymers or lipid-like materials, that will subsequently form NPs by self-assembly or chemical conjugation processes. The current review highlights the impact of high-throughput in the development of more efficient drug delivery systems with enhanced targeting and release kinetics. It also describes the current challenges in this research area as well as future directions.

Biomaterials by design: Harnessing data for future development

Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.