Mapping Drug-Induced Neuropathy through In-Situ Motor Protein Tracking and Machine Learning

Yi, Zhigao; Gao, Huxin; Ji, Xianglin; Yeo, Xin-Yi; Chong, Suet Yen; Mao, Yu-Jie; Luo, Baiwen; Shen, Chao; Han, Sanyang; Wang, Jiong‐Wei; Jung, Sangyong; Shi, Peng; Ren, Hongliang; Liu, Xiaogang

doi:10.1021/jacs.1c07312

Cited by 11 publications

(9 citation statements)

References 40 publications

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“…Machine learning (ML) based regression techniques are becoming wide spread in many areas of data analysis in the chemical 11,12 and pharmaceutical sector [13][14][15][16] ; they have recently been employed in drug development [17][18][19] , diagnostic 20 , treatment algorithm optimisation 21 , drug repurposing 2,22 and material discovery 23,24 ; however such applications are still quite limited despite being very promising 25,26 . Another application of ML technologies in drug discovery is during compound screening or hit/lead generation and optimization enabling a virtual screening platform that offers a quicker and cheaper alternative to classic testing of large compounds libraries 27,28 ; virtual screening can be generally classified in ligand-based or structure-based 28 .…”

Section: Feasibility and Application Of Machine Learning Enabled Fast...mentioning

confidence: 99%

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

Perni

Prokopovich

2022

Sci Rep

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Despite the large prevalence of diseases affecting cartilage (e.g. knee osteoarthritis affecting 16% of population globally), no curative treatments are available because of the limited capacity of drugs to localise in such tissue caused by low vascularisation and electrostatic repulsion. While an effective delivery system is sought, the only option is using high drug doses that can lead to systemic side effects. We introduced poly-beta-amino-esters (PBAEs) to effectively deliver drugs into cartilage tissues. PBAEs are copolymer of amines and di-acrylates further end-capped with other amine; therefore encompassing a very large research space for the identification of optimal candidates. In order to accelerate the screening of all possible PBAEs, the results of a small pool of polymers (n = 90) were used to train a variety of machine learning (ML) methods using only polymers properties available in public libraries or estimated from the chemical structure. Bagged multivariate adaptive regression splines (MARS) returned the best predictive performance and was used on the remaining (n = 3915) possible PBAEs resulting in the recognition of pivotal features; a further round of screening was carried out on PBAEs (n = 150) with small variations of structure of the main candidates from the first round. The refinements of such characteristics enabled the identification of a leading candidate predicted to improve drug uptake > 20 folds over conventional clinical treatment; this uptake improvement was also experimentally confirmed. This work highlights the potential of ML to accelerate biomaterials development by efficiently extracting information from a limited experimental dataset thus allowing patients to benefit earlier from a new technology and at a lower price. Such roadmap could also be applied for other drug/materials development where optimisation would normally be approached through combinatorial chemistry.

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Section: Feasibility and Application Of Machine Learning Enabled Fast...mentioning

confidence: 99%

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

Perni

Prokopovich

2022

Sci Rep

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“…20 On the basis of this upconversion microscopy system, we have developed an in situ neuropathy assessment platform for drug screening. 21 In particular, UCNP-mediated optogenetic techniques described above eliminate the need for fiber optical implantation surgeries without compromising the depth of tissue penetration for visible light transmission, greatly simplifying the overall operation.…”

Section: Upconversion-mediated Optogeneticsmentioning

confidence: 99%

“…Recently, we developed a real-time cargo tracking system for sensory neurons using upconversion nanoprobes, showing promise for the diagnosis of neurodegenerative disorders . On the basis of this upconversion microscopy system, we have developed an in situ neuropathy assessment platform for drug screening …”

Section: Photoconversion Manipulation Of Light-sensitive Ion Channelsmentioning

confidence: 99%

Noninvasive Manipulation of Ion Channels for Neuromodulation and Theranostics

Liu

Yao

et al. 2022

Acc. Mater. Res.

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Metrics & More Article Recommendations CONSPECTUS: Functional and structural studies of ion channels have deepened our understanding of their mechanisms and important role in regulating neuronal activity and treating disease. Manipulation of ion channels can directly control electrochemical signals in the nervous and cardiovascular systems for advanced neuromodulation and theranostics. Manipulation tools based on ion channel control, such as optogenetics, electrical stimulation, chemogenetics, and gene editing, have advanced basic biomedical research and enabled unprecedented treatment strategies. Nevertheless, conventional approaches are associated with limitations such as invasiveness, irreversibility, or low spatiotemporal resolution, which limit their clinical application. Therefore, targeted noninvasive or minimally invasive modulation of various ion channel activities is highly desirable. In recent years, nanomaterials have enabled noninvasive and precise modulation of ion channels due to their tunable morphology and physicochemical properties. Based on activation modes, current nanomaterial-based methods for ion channel control include light stimulation, thermal regulation, and mechanical manipulation. Nanomaterials can serve as energy transducers to convert macroscopic signals that penetrate deep into tissue, such as near-infrared light or magnetic fields, into local stimuli for ion channel manipulation. For photomodulation, lanthanide-doped nanoparticles convert near-infrared light into visible energy by upconversion, enabling bidirectional control of excitatory and inhibitory opsins in deep tissues. For thermal modulation, photothermal nanoparticles transform absorbed light, such as near-infrared, into heat to activate thermosensitive channels in deep targets. For mechanical manipulation, magnetic nanoparticles convert external magnetic fields into local attractive and torque forces to regulate the functionality of mechanosensitive channels without concern for penetration depth. Moreover, nanomaterial-based strategies can realize targeted and minimally invasive control over the activities of various ion channels even at the single-cell level, maximizing their feasibility and application potential.In this Account, we focus on the manipulation of ion channels using nanomaterials and categorize current approaches into photoconversion optogenetics, thermogenetics, and magnetogenetics. We first introduce the biological principles of various opsins, discuss thermo-and mechanosensitive ion channels and describe their molecular mechanisms in the context of channel structures. We also outline the general principles of emerging nanomaterials in terms of energy conversion capability. We highlight the major advances in nanoprobe-enabled systems and their modern applications in neuromodulation and theranostics. We conclude the Account with a discussion of existing challenges and future prospects.

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“…In the future, we expect the combination of large biological and analytical data and artificial intelligence such as machine learning to guide drug discovery and to produce more efficient and safer drugs. In a recent example, the combination of a near-infrared upconversion nanoparticle-based imaging platform and machine learning enabled mapping chemotherapy-induced peripheral neuropathy to assess drug-induced neurotoxicity …”

mentioning

confidence: 99%

“…In a recent example, the combination of a near-infrared upconversion nanoparticle-based imaging platform and machine learning enabled mapping chemotherapyinduced peripheral neuropathy to assess drug-induced neurotoxicity. 78 In the future, we expect the combination of large biological and analytical data and artificial intelligence such as machine learning to guide drug discovery and to produce more efficient and safer drugs.…”

mentioning

confidence: 99%