Mid-infrared Nanoantennas as Ultrasensitive Vibrational Probes Assisted by Machine Learning and Hyperspectral Imaging

Abstract Infrared (IR) Spectroscopy has been developed for centuries and has been widely used to identify molecular structure from the massive information provided by IR fingerprint absorption, reflecting the vibration energy of the chemical bond. Due to the intrinsically weak light-matter interaction, IR spectroscopy serves low sensitivity and sizeable optical interaction length (~mm to ~cm) compared with other optical probes like Raman, florescent, and refractometry technology, which hinder the applications for ultra-sensitive biomolecular screening. Here, we report a new type of IR spectroscopy by wavelength gradient hook nanoantenna integrated with the microfluidic channel, enhancing the IR molecular absorption and bringing in refractometry function with ultrathin (~100 nm) optical interaction length. With the proof-of-concept demonstration of molecular recognition of mixed alcoholic liquids by machine learning and molecular fingerprint retrieving by hyperspectral images in one-time data acquisition, our work paves the way to advance, small-volume, real-time, ultra-sensitive, in-vitro biomolecular dynamic analysis in the aqueous environment.

Download Full-text

Hydrogels: A Versatile Drug Delivery Carrier Systems

International Journal of Pharmaceutical Sciences and Nanotechnology ◽

10.37285/ijpsn.2012.5.3.1 ◽

1970 ◽

Vol 5 (3) ◽

pp. 1745-1756

Author(s):

L H Baldaniya ◽

Sarkhejiya N A

Keyword(s):

Drug Delivery ◽

Protein Delivery ◽

Structural Integrity ◽

Healing Process ◽

Polymer Chains ◽

Wound Healing Process ◽

Aqueous Environment ◽

Preparation Methods ◽

Control Drug

Hydrogels are the material of choice for many applications in regenerative medicine due to their unique properties including biocompatibility, ﬂexible methods of synthesis, range of constituents, and desirable physical characteristics. Hydrogel (also called Aquagel) is a network of polymer chains that are hydrophilic, sometimes found as a colloidal gel in which water is the dispersion medium. Hydrogels are highly absorbent (contain ~99.9% water), natural or synthetic polymers. Hydrogel also possess a degree of flexibility very similar to natural tissue, due to its significant water content. It can serve as scaffolds that provide structural integrity to tissue constructs, control drug and protein delivery to tissues and cultures. Also serve as adhesives or barriers between tissue and material surfaces. The positive effect of hydrogels on wounds and enhanced wound healing process has been proven. Hydrogels provide a warm, moist environment for wound that makes it heal faster in addition to its useful mucoadhesive properties. Moreover, hydrogels can be used as carriers for liposomes containing variety of drugs, such as antimicrobial drugs. Hydrogels are water swollen polymer matrices, with a tendency to imbibe water when placed in aqueous environment. This ability to swell, under biological conditions, makes it an ideal material for use in drug delivery and immobilization of proteins, peptides, and other biological compounds. Hydrogels have been extensively investigated for use as constructs to engineer tissues in vitro. This review describes the properties, classification, preparation methods, applications, various monomer used in formulation and development of hydrogel products.

Download Full-text

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity

Current Drug Discovery Technologies ◽

10.2174/1570163816666190411110331 ◽

2020 ◽

Vol 17 (3) ◽

pp. 365-375

Author(s):

Vasyl Kovalishyn ◽

Diana Hodyna ◽

Vitaliy O. Sinenko ◽

Volodymyr Blagodatny ◽

Ivan Semenyuta ◽

...

Keyword(s):

Machine Learning ◽

Isonicotinic Acid ◽

Acid Hydrazide ◽

Predictive Ability ◽

Antitubercular Activity ◽

Biological Evaluation ◽

Starting Point ◽

Binary Classifiers ◽

Synthesis And Biological Evaluation

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

Download Full-text

Computational Methods for Structure-to-Function Analysis of Diet-Derived Catechins-Mediated Targeting of In Vitro Vasculogenic Mimicry

Cancer Informatics ◽

10.1177/11769351211009229 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110092

Author(s):

Abicumaran Uthamacumaran ◽

Narjara Gonzalez Suarez ◽

Abdoulaye Baniré Diallo ◽

Borhane Annabi

Keyword(s):

Machine Learning ◽

Cancer Cells ◽

Structural Changes ◽

Function Analysis ◽

Vasculogenic Mimicry ◽

Machine Learning Algorithms ◽

Emergent Behavior ◽

Molecular Signature ◽

Ovarian Cancer Cells

Background: Vasculogenic mimicry (VM) is an adaptive biological phenomenon wherein cancer cells spontaneously self-organize into 3-dimensional (3D) branching network structures. This emergent behavior is considered central in promoting an invasive, metastatic, and therapy resistance molecular signature to cancer cells. The quantitative analysis of such complex phenotypic systems could require the use of computational approaches including machine learning algorithms originating from complexity science. Procedures: In vitro 3D VM was performed with SKOV3 and ES2 ovarian cancer cells cultured on Matrigel. Diet-derived catechins disruption of VM was monitored at 24 hours with pictures taken with an inverted microscope. Three computational algorithms for complex feature extraction relevant for 3D VM, including 2D wavelet analysis, fractal dimension, and percolation clustering scores were assessed coupled with machine learning classifiers. Results: These algorithms demonstrated the structure-to-function galloyl moiety impact on VM for each of the gallated catechin tested, and shown applicable in quantifying the drug-mediated structural changes in VM processes. Conclusions: Our study provides evidence of how appropriate 3D VM compression and feature extractors coupled with classification/regression methods could be efficient to study in vitro drug-induced perturbation of complex processes. Such approaches could be exploited in the development and characterization of drugs targeting VM.

Download Full-text

Improve Irrigation Timing Decision for Agriculture using Real Time Data and Machine Learning

2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) ◽

10.1109/icdabi51230.2020.9325680 ◽

2020 ◽

Author(s):

Joao Cardoso ◽

Andre Gloria ◽

Pedro Sebastiao

Keyword(s):

Machine Learning ◽

Real Time ◽

Time Data ◽

Real Time Data ◽

Timing Decision

Download Full-text

Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

Reproductive Biology and Endocrinology ◽

10.1186/s12958-021-00734-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Qingsong Xi ◽

Qiyu Yang ◽

Meng Wang ◽

Bo Huang ◽

Bo Zhang ◽

...

Keyword(s):

Machine Learning ◽

In Vitro Fertilization ◽

Endometrial Thickness ◽

Learning System ◽

Embryo Selection ◽

Selection Strategy ◽

Application Study ◽

Vitro Fertilization ◽

The Impact

Abstract Background To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes. Results For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

Download Full-text

Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽

10.3390/molecules26092505 ◽

2021 ◽

Vol 26 (9) ◽

pp. 2505

Author(s):

Raheem Remtulla ◽

Sanjoy Kumar Das ◽

Leonard A. Levin

Keyword(s):

Machine Learning ◽

Neural Networks ◽

In Silico ◽

Disulfide Bonds ◽

Oral Absorption ◽

In Vivo Studies ◽

Drug Candidates ◽

In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

Download Full-text

Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1

Molecules ◽

10.3390/molecules24152747 ◽

2019 ◽

Vol 24 (15) ◽

pp. 2747 ◽

Cited By ~ 1

Author(s):

Eliane Briand ◽

Ragnar Thomsen ◽

Kristian Linnet ◽

Henrik Berg Rasmussen ◽

Søren Brunak ◽

...

Keyword(s):

Machine Learning ◽

Drug Interactions ◽

Scoring Function ◽

Docking Study ◽

Inhibitory Effect ◽

Therapeutic Agents ◽

Learning Approaches ◽

Ensemble Docking ◽

Ic50 Values

The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.

Download Full-text

The Implications of Regulatory Framework for Topical Semisolid Drug Products: From Critical Quality and Performance Attributes towards Establishing Bioequivalence

Pharmaceutics ◽

10.3390/pharmaceutics13050710 ◽

2021 ◽

Vol 13 (5) ◽

pp. 710

Author(s):

Tanja Ilić ◽

Ivana Pantelić ◽

Snežana Savić

Keyword(s):

Statistical Power ◽

Failure Modes ◽

In Vitro Release ◽

Optimal Number ◽

Drug Products ◽

Pharmaceutical Equivalence ◽

And Performance ◽

Low Sensitivity

Due to complex interdependent relationships affecting their microstructure, topical semisolid drug formulations face unique obstacles to the development of generics compared to other drug products. Traditionally, establishing bioequivalence is based on comparative clinical trials, which are expensive and often associated with high degrees of variability and low sensitivity in detecting formulation differences. To address this issue, leading regulatory agencies have aimed to advance guidelines relevant to topical generics, ultimately accepting different non-clinical, in vitro/in vivo surrogate methods for topical bioequivalence assessment. Unfortunately, according to both industry and academia stakeholders, these efforts are far from flawless, and often upsurge the potential for result variability and a number of other failure modes. This paper offers a comprehensive review of the literature focused on amending regulatory positions concerning the demonstration of (i) extended pharmaceutical equivalence and (ii) equivalence with respect to the efficacy of topical semisolids. The proposed corrective measures are disclosed and critically discussed, as they span from mere demands to widen the acceptance range (e.g., from ±10% to ±20%/±25% for rheology and in vitro release parameters highly prone to batch-to-batch variability) or reassess the optimal number of samples required to reach the desired statistical power, but also rely on specific data modeling or novel statistical approaches.

Download Full-text