An Ultrafast and Flexible LC-MS/MS System Paves the Way for Machine Learning Driven Sample Processing and Data Evaluation in Early Drug Discovery

Rationale: Low speed and flexibility of most LC-MS/MS approaches in early drug discovery delays sample analysis from routine in vivo studies within the same day of measurements. A highthroughput platform for the rapid quantification of drug compounds in various in vivo assays was developed and established in routine bioanalysis. Methods: Automated selection of an efficient and adequate LC method was realized by autonomous sample qualification for ultrafast batch gradients (9 s/sample) or for fast linear gradients (45 s/sample) if samples require chromatography. The hardware and software components of our Rapid and Integrated Analysis System (RIAS) were streamlined for increased analytical throughput via state-of-the-art automation while keeping high analytical quality. Results: Online decision-making was based on a quick assay suitability test (AST) based on a small and dedicated sample set evaluated by two different strategies. 84% of the acquired data points were within ±30% accuracy and 93% of the deviations between the lower limit of quantitation (LLOQ) values were ≤2-fold compared to standard LC-MS/MS systems while speed, flexibility and overall automation was significantly improved. Conclusions: The developed platform provided an analysis time of only 10 min (batch-mode) and 50 min (gradient-mode) per standard pharmacokinetic (PK) study (62 injections). Automation, data evaluation and results handling were optimized to pave the way for machine learning based decision-making regarding the evaluation strategy of the AST

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An ultrafast and flexible LC‐MS/MS system paves the way for machine learning driven in vivo sample processing and data evaluation in early drug discovery

Rapid Communications in Mass Spectrometry ◽

10.1002/rcm.9096 ◽

2021 ◽

Author(s):

Tim T. Häbe ◽

Christian Späth ◽

Steffen Schrade ◽

Wolfgang Jörg ◽

Roderich D. Süssmuth ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Data Evaluation ◽

Sample Processing ◽

Early Drug ◽

The Way

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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.

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Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery

Journal of Medicinal Chemistry ◽

10.1021/acs.jmedchem.0c02033 ◽

2021 ◽

Vol 64 (6) ◽

pp. 3185-3196

Author(s):

Emile P. Chen ◽

Robert W. Bondi ◽

Paul J. Michalski

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Medicinal Chemistry ◽

Model Based ◽

Early Drug

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Application of HPLC measurements for the determination of physicochemical and biomimetic properties to model in vivo drug distribution in support of early drug discovery

Handbook of Analytical Separations - Separation Methods in Drug Synthesis and Purification ◽

10.1016/b978-0-444-64070-3.00013-8 ◽

2020 ◽

pp. 667-758

Author(s):

Klára L. Valkó

Keyword(s):

Drug Discovery ◽

Drug Distribution ◽

Early Drug

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Improving machine learning in early drug discovery

Annals of Mathematics and Artificial Intelligence ◽

10.1007/s10472-017-9541-2 ◽

2017 ◽

Vol 81 (1-2) ◽

pp. 155-166 ◽

Cited By ~ 3

Author(s):

Claus Bendtsen ◽

Andrea Degasperi ◽

Ernst Ahlberg ◽

Lars Carlsson

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Early Drug

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Recombinant Adeno Associated Viral (AAV) vector type 9 delivery of Ex1-Q138-mutant huntingtin in the rat striatum as a short-time model for in vivo studies in drug discovery

Neurobiology of Disease ◽

10.1016/j.nbd.2015.11.019 ◽

2016 ◽

Vol 86 ◽

pp. 41-51 ◽

Cited By ~ 3

Author(s):

Ilaria Ceccarelli ◽

Pasquale Fiengo ◽

Rosaria Remelli ◽

Vincenzo Miragliotta ◽

Lara Rossini ◽

...

Keyword(s):

Drug Discovery ◽

In Vivo Studies ◽

Rat Striatum ◽

Mutant Huntingtin ◽

Time Model ◽

Aav Vector ◽

Vector Type ◽

Short Time

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In vivo Neuropathology: Detecting the Neurotoxicity of Candidate Drugs during Early Drug Discovery

10.33696/neurol.2.052 ◽

2021 ◽

Vol 2 (4) ◽

Keyword(s):

Drug Discovery ◽

Early Drug

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Machine Learning Techniques for Corneal Diseases Diagnosis: A Survey

International Journal of Image and Graphics ◽

10.1142/s0219467821500169 ◽

2020 ◽

pp. 2150016

Author(s):

Samer Kais Jameel ◽

Sezgin Aydin ◽

Nebras H. Ghaeb

Keyword(s):

Machine Learning ◽

Decision Making ◽

Clinical Data ◽

Medical Images ◽

Diagnostic Techniques ◽

Machine Learning Techniques ◽

Corneal Disease ◽

Automated Learning ◽

Learning Techniques ◽

The Way

Machine learning techniques become more related to medical researches by using medical images as a dataset. It is categorized and analyzed for ultimate effectiveness in diagnosis or decision-making for diseases. Machine learning techniques have been exploited in numerous researches related to corneal diseases, contribution to ophthalmologists for diagnosing the diseases and comprehending the way automated learning techniques act. Nevertheless, confusion still exists in the type of data used, whether it is images, data extracted from images or clinical data, the course reliant on the type of device for obtaining them. In this study, the researches that used machine learning were reviewed and classified in terms of the kind of utilized machine for capturing data, along with the latest updates in sophisticated approaches for corneal disease diagnostic techniques.

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