Machine learning-integrated omics for the risk and safety assessment of nanomaterials

Machine learning assisted designing of conjugated organic chromophores, light absorption, and emission behavior prediction

Organic materials have several important characteristics that make them suitable for use in optoelectronics and optical signal processing applications. For absorption and emission maxima, the stabilities and photoactivities of conjugated organic chromophores can be tailored by selecting a suitable parent structure and incorporating substituents that predictably change the optical characteristics. However, a high-throughput design of efficient conjugated organic chromophores without using trial-and-error experimental approaches is required. In this study, machine learning (ML) is used to design and test the conjugated organic chromophores and predict light absorption and emission behavior. Many machine learning models are tried to select the best models for the prediction of absorption and emission maxima. Extreme gradient boosting regressor has appeared as the best model for the prediction of absorption maxima. Random forest regressor stands out as the best model for the prediction of emission maxima. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) is used to generate 10,000 organic chromophores. Chemical similarity analysis is performed to obtain a deeper understanding of the characteristics and actions of compounds. Furthermore, clustering and heatmap approaches are utilized.

Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions

Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.

Advancements in nanomedicine: Precision delivery strategies for male pelvic malignancies - Spotlight on prostate and colorectal cancer

BACKGROUND

Pelvic malignancies consistently pose significant global health challenges, adversely affecting the well-being of the male population. It is anticipated that clinicians will continue to confront these cancers in their practice. Nanomedicine offers promising strategies that revolutionize the treatment of male pelvic malignancies by providing precise delivery methods that aim to improve the efficacy of therapeutic outcomes while minimizing side effects. Nanoparticles are designed to encapsulate therapeutic agents and selectively target cancer cells. They can also be loaded with theragnostic agents, enabling multifunctional capabilities.

OBJECTIVE

This review aims to summarize the latest nanomedicine research into clinical applications, focusing on nanotechnology-based treatment strategies for male pelvic malignancies, encompassing chemotherapy, radiotherapy, immunotherapy, and other cutting-edge therapies. The review is structured to assist physicians, particularly those with limited knowledge of biochemistry and bioengineering, in comprehending the functionalities and applications of nanomaterials.

METHODS

Multiple databases, including PubMed, the National Library of Medicine, and Embase, were utilized to locate and review recently published articles on advancements in nano-drug delivery for prostate and colorectal cancers.

CONCLUSION

Nanomedicine possesses considerable potential in improving therapeutic outcomes and reducing adverse effects for male pelvic malignancies. Through precision delivery methods, this emerging field presents innovative treatment modalities to address these challenging diseases. Nevertheless, the majority of current studies are in the preclinical phase, with a lack of sufficient evidence to fully understand the precise mechanisms of action, absence of comprehensive pharmacotoxicity profiles, and uncertainty surrounding long-term consequences.

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.

Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles

Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding that a bag of artificial neural networks (ANNs) can provide such a prediction when such machines are trained with a relatively small data set (with ca. 200 examples). Therein, viabilities were predicted by consensus using the weighted means of the predictions from the bags. Here, we confirm the accuracy and precision of the prediction of nanoparticle viabilities using an optimized bag of ANNs over sets of data examples that had not previously been used in the training and validation process. We also introduce the viability strip, rather than a single value, as the prediction and construct it from the viability probability distribution of an ensemble of ANNs compatible with the data set. Specifically, the ensemble consists of the ANNs arising from subsets of the data set corresponding to different splittings between training and validation, and the different bags (k-folds). A k - 1 k machine uses a single partition (or bag) of k - 1 ANNs each trained on 1/k of the data to obtain a consensus prediction, and a k-bag machine quorum samples the k possible k - 1 k machines available for a given partition. We find that with increasing k in the k-bag or k - 1 k machines, the viability strips become more normally distributed and their predictions become more precise. Benchmark comparisons between ensembles of 4-bag machines and 3 4 fraction machines suggest that the 3 4 fraction machine has similar accuracy while overcoming some of the challenges arising from divergent ANNs in the 4-bag machines.

Integrative approaches based on genomic techniques in the functional studies on enhancers

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.

Generation of Chemical Space of Compounds for Prostate Cancer Treatment: Biological Activity Prediction, Clustering, and Visualization of Chemical Space

Designing molecules for pharmaceutical purposes has been a significant focus for several decades. The pursuit of novel drugs is an arduous and financially demanding undertaking. Nevertheless, the integration of computer-assisted frameworks presents a swift avenue for designing and screening drug-like compounds. Within the context of this research, we introduce a comprehensive approach for the design and screening of compounds tailored to the treatment of prostate cancer. To forecast the biological activity of these compounds, we employed machine learning (ML) models. Additionally, an automated process involving the deconstruction and reconstruction of molecular building blocks leads to the generation of novel compounds. Subsequently, the ML models were utilized to predict the biological activity of the designed compounds, and the t-SNE method was employed to visualize the chemical space covered by the novel compounds. A meticulous selection process identified the most promising compounds, and their potential for synthesis was assessed, offering valuable guidance to experimental chemists in their investigative endeavors. Furthermore, fingerprint and heatmap analysis were conducted to evaluate the chemical similarity among the selected compounds. This multifaceted approach, encompassing predictive modeling, compound generation, visualization, and similarity assessment, underscores our commitment to refining the process of identifying potential candidates for further exploration in prostate cancer treatment.

Computational method for aromatase-related proteins using machine learning approach

Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.

Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds

Designing molecules for drugs has been a hot topic for many decades. However, it is hard and expensive to find a new molecule. Thus, the cost of the final drug is also increased. Machine learning can provide the fastest way to predict the biological activity of druglike molecules. In the present work, machine learning models are trained for the prediction of the biological activity of aromatase inhibitors. Data was collected from the literature. Molecular descriptors are calculated to be used as independent features for model training. The results showed that the R ² values for linear regression, random forest regression, gradient boosting regression, and bagging regression are 0.58, 0.84, 0.77, and 0.80, respectively. Using these models, it is possible to predict the activity of new molecules in a short period of time and at a reasonable cost. Furthermore, Tanimoto similarity is used for similarity analysis, as well as a chemical database is mined to search for similar molecules. Nonetheless, this study provides a framework for repurposing other effective drug molecules to prevent cancer.

Investigation of the Anticancer Potential of 2-alkoxycarbonylallyl Esters Against Metastatic Murine Breast Cancer Line 4T1 Targeting the EGFR: A Combined Molecular Docking, QSAR, and Machine Learning Approach

Background:

The search for novel and potent anticancer drugs is imperative. This present study aims to unravel the mechanisms of action of 2-alkoxyecarbonyl esters using robust model(s) that can accurately predict the bioactivity of novel compounds. Twenty-four potential anticancer 2- alkoxycarbonylallyl ester compounds obtained from the literature were employed in building a 3D-QSAR model.

Objectives:

The objective of this study is to determine the predictive ability of the GFA-based QSAR models and extreme machine learning models and compare them. The lead compounds and newly designed compounds were docked at the active site of a human epidermal growth factor receptor (EGFR) kinase domain to determine their binding modes and affinity.

Methods:

QikProp program and Spartan packages were employed for screening compounds for druglikeness and toxicity. QSAR models were equally used to predict the bioactivities of these molecules using the Material Studio package. Molecular docking of the molecules at the active site of an EGFR receptor, 1M17, was done using Auto dock tools.

Results:

The model of choice, with r2pred (0.857), satisfied the recommended standard for a stable and reliable model. The low value of r2, Q2 for several trials and cRp2 (0.779 ≥ 0.5) and the high value of correlation coefficient r2 for the training set (0.918) and test set (0.849) provide credence to the predictability of the model. The superior inhibition of EGFR displayed by the lead compounds (20 and 21) with binding energies of 6.70 and 7.00 kcalmol-1, respectively, is likely due to the presence of double bonds and α-ester groups. ADMET screening showed that these compounds are highly druggable. The designed compounds (A and B) displayed better inhibition of EGFR.

Conclusion:

The QSAR model used here performed better than the Random Forest Regression model for predicting the bioactivity of these anticancer compounds, while the designed compounds (A and B) performed better with higher binding affinity than the lead compounds. Implementing the developed model would be helpful in the search for novel anticancer agents.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Safety-by-design using forward and inverse multi-target machine learning

The economic and social future of nanotechnology depends on our ability and manufacture nanomaterials that avoid potential toxicity, by identifying them before they are made, used and released into the environment. Safety-by-design is a framework for including these issues at an early stage of the development process, but balancing multiple nanoparticle properties and selection criteria remains challenging. Based on a synthetic data set of over 19,000 possible sunscreen product specifications, we have used multi-target machine learning to predict the corresponding size, shape, concentration and polytype of titania nanoparticle additives. The study considers the optical properties responsible for the sun protection factor and product transparency, including the extinction coefficients for ultra violet and visible light, and the potential for toxicity due to the generation of reactive oxygen species from the photocatalytically active facets of both anatase and rutile nanoparticles, as a function of the size and shape. We predict a number of conventional forward structure/property and property/product relationships, but show that a direct structure/product relationship provides superior performance when predicting multiple properties or product specifications simultaneously. These models are then inverted, re-optimized and re-trained to provide focused, high performing inverse design models that do not require additional optimization, and are capable of identifying nanoparticle configurations outside of the training set. The ability to directly predict suitable nanoparticle structures that conform to prerequisite sun protection, transparently and potential toxicity thresholds represents a new approach to safety-by-design that can be applied to other products and materials where multiple design criteria must be met at the same time.

Exploring flexibility, intermolecular interactions and ADMET profiles of anti-influenza agent isorhapontigenin: A quantum chemical and molecular docking study

Isorhapontigenin (IRPG) drug emerges as promising efficient inhibitor for H1N1 and H3N2 subtypes which belong to influenza A virus; reported with IC₅₀ value of 35.62 and 63.50 μM respectively. When experimental data are compared to the predicted geometrical parameters and vibrational assignments (FT-IR and FT-Raman), the findings indicated a strong correlation. The absorption bands of π→π∗ transitions are revealed through UV-Vis electronic properties; this confirms that the IRPG molecule shows strong bands. Through NBO and HOMO-LUMO analysis, the kinetic stability and chemical reactivity of the IRPG molecule were investigated. By using an MEP map, the IRPG's electrophilic and nucleophilic site selectivity was assessed. In a molecular docking investigation, the IRPG molecule shows a stronger inhibition constant and binding affinity for the H1N1 and H3N2 influenza virus. The IRPG molecule thus reveals good biological actions in nature and can be used as a potential therapeutic drug candidate for H1N1 and H3N2 virus A influenza.

Climate change impact on fungi in the atmospheric microbiome

The atmospheric microbiome is one of the least studied microbiomes of our planet. One of the most abundant, diverse and impactful parts of this microbiome is arguably fungal spores. They can be very potent outdoor aeroallergens and pathogens, causing an enormous socio-economic burden on health services and annual damages to crops costing billions of Euros. We find through hypothesis testing that an expected warmer and drier climate has a dramatic impact on the atmospheric microbiome, conceivably through alteration of the hydrological cycle impacting agricultural systems, with significant differences in leaf wetness between years (p-value <0.05). The data were measured via high-throughput sequencing analysis using the DNA barcode marker, ITS2. This was complemented by remote sensing analysis of land cover and dry matter productivity based on the Sentinel satellites, on-site detection of atmospheric and vegetation variables, GIS analysis, harvesting analysis and footprint modelling on trajectory clusters using the atmospheric transport model HYSPLIT. We find the seasonal spore composition varies between rural and urban zones reflecting both human activities (e.g. harvest), type and status of the vegetation and the prevailing climate rather than mesoscale atmospheric transport. We find that crop harvesting governs the composition of the atmospheric microbiome through a clear distinction between harvest and post-harvest beta-diversity by PERMANOVA on Bray-Curtis dissimilarity (p-value <0.05). Land cover impacted significantly by two-way ANOVA (p-value <0.05), while there was minimal impact from air mass transport over the 3 years. The hypothesis suggests that the fungal spore composition will change dramatically due to climate change, an until now unforeseen effect affecting both food security, human health and the atmospheric hydrological cycle. Consequently the management of crop diseases and impact on human health through aeroallergen exposure need to consider the timing of crop treatments and land management, including post harvest, to minimize exposure of aeroallergens and pathogens.

Leveraging the advancements in functional biomaterials and scaffold fabrication technologies for chronic wound healing applications

Exploring new avenues for clinical management of chronic wounds holds the key to eliminating socioeconomic burdens and health-related concerns associated with this silent killer. Engineered biomaterials offer great promise for repair and regeneration of chronic wounds because of their ability to deliver therapeutics, protect the wound environment, and support the skin matrices to facilitate tissue growth. This mini review presents recent advances in biomaterial functionalities for enhancing wound healing and demonstrates a move from sub-optimal methods to multi-functionalized treatment approaches. In this context, we discuss the recently reported biomaterial characteristics such as bioadhesiveness, antimicrobial properties, proangiogenic attributes, and anti-inflammatory properties that promote chronic wound healing. In addition, we highlight the necessary mechanical and mass transport properties of such biomaterials. Then, we discuss the characteristic properties of various biomaterial templates, including hydrogels, cryogels, nanomaterials, and biomolecule-functionalized materials. These biomaterials can be microfabricated into various structures, including smart patches, microneedles, electrospun scaffolds, and 3D-bioprinted structures, to advance the field of biomaterial scaffolds for effective wound healing. Finally, we provide an outlook on the future while emphasizing the need for their detailed functional behaviour and inflammatory response studies in a complex in vivo environment for superior clinical outcomes and reduced regulatory hurdles.

From the pills to environment - Prediction and tracking of non-steroidal anti-inflammatory drug concentrations in wastewater

The extend of environment pollution by pharmaceuticals is in a stage that required more automatic and integrated solutions. The non-steroidal anti-inflammatory drugs (NSAIDs) are one of the most popular pharmaceutical in the world and emerging pollutants of natural waters. The aim of the paper was to check the correlation of the sales data of selected NSAIDs (ibuprofen, naproxen, diclofenac) and their concentration in the WWTP in order to enable predicting their loads, having only the sales data. For calculations, we apply three discharge scenarios (the fates between purchased to the presence in influents), having in mind that some part of sold mass can be improperly dispose to sewage system. To support predictions, chemical analysis was conducted in two conventional wastewater treatment plants (WWTPs) located in Poland during 2018 and 2020, thereby before and during pandemic situation. The NSAIDs concentration in the influent was higher than that which would be obtained if all of the administrated mass of the pharmaceutical went through the metabolic pathway of transformation. This means that substantial mass of sold NSAIDs in improperly dispose to sewage system, and this factor need to be taken into account in future predictions. Furthermore, results indicate that the variance of naproxen and diclofenac concentrations in the influent has no correlation with relatively stable sales throughout whole year. The pandemic situation had yet no direct effect to diclofenac concentrations in influents, despite observed increasing of sales. It was calculated that more than 60 kg of diclofenac was discharged into the Baltic Sea in 2018, and 20 kg in the first half of 2021 from two tested WWTPs. The presence of 4OH-diclofenac in effluents often in higher concentration compared to diclofenac mean that this still biologically active compound need to be taken into account in future risk assessment.

Developing Efficient Small Molecule Acceptors with sp2 -Hybridized Nitrogen at Different Positions by Density Functional Theory Calculations, Molecular Dynamics Simulations and Machine Learning

Chemical structure of small molecule acceptors determines their performance in organic solar cells. Multiscale simulations are necessary to avoid trial-and-error based design, ultimately to save time and resources. In current study, the effect of sp² -hybridized nitrogen substitution at the inner or the outmost position of central core, side chain, and terminal group of small molecule acceptors is investigated using multiscale computational modelling. Quantum chemical analysis is used to study the electronic behavior. Nitrogen substitution at end-capping has significantly decreased the electron-reorganization energy. No big change is observed in transfer integral and excited state behavior. However, nitrogen substitution at terminal group position is good way to improve electron-mobility. Power conversion efficiency (PCE) of newly designed acceptors is predicted using machine learning. Molecular dynamics simulations are also performed to explore the dynamics of acceptor and their blends with PBDB-T polymer donor. Florgy-Huggins parameter is calculated to study the mixing of designed small molecule acceptors with PBDB-T. Radial distribution function has indicated that PBDB-T has a closer packing with N3 and N4. From all analysis, it is found that nitrogen substitution at end-capping group is a better strategy to design efficient small molecule acceptors.

Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

Two-dimensional (2D) materials have emerged as an important class of nanomaterials for technological innovation due to their remarkable physicochemical properties, including sheet-like morphology and minimal thickness, high surface area, tuneable chemical composition, and surface functionalization. These materials are being proposed for new applications in energy, health, and the environment; these are all strategic society sectors toward sustainable development. Specifically, 2D materials for nano-imaging have shown exciting opportunities in in vitro and in vivo models, providing novel molecular imaging techniques such as computed tomography, magnetic resonance imaging, fluorescence and luminescence optical imaging and others. Therefore, given the growing interest in 2D materials, it is mandatory to evaluate their impact on the immune system in a broader sense, because it is responsible for detecting and eliminating foreign agents in living organisms. This mini-review presents an overview on the frontier of research involving 2D materials applications, nano-imaging and their immunosafety aspects. Finally, we highlight the importance of nanoinformatics approaches and computational modeling for a deeper understanding of the links between nanomaterial physicochemical properties and biological responses (immunotoxicity/biocompatibility) towards enabling immunosafety-by-design 2D materials.