Phenotypic drug discovery: recent successes, lessons learned and new directions

Vincent, Fabien; Nueda, Arsenio; Lee, Jonathan; Schenone, Monica; Prunotto, Marco; Mercola, Mark

doi:10.1038/s41573-022-00472-w

Cited by 101 publications

(121 citation statements)

References 219 publications

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“…CO-ADD's phenotypic screening approach, traditionally used for antimicrobial discovery, is currently undergoing a renaissance for other therapeutic areas. 113 Together with our recent studies, this work strongly supports further investigations into metal complexes as potential antimicrobial agents. We encourage other researchers to conduct more extensive studies into these compound classes.…”

Section: ■ Conclusionsupporting

confidence: 82%

“…We do note that the selection of metal complexes tested is biased by the collaborator’s motive in making and submitting compounds, but the same caveat applies to submitted organic compounds. CO-ADD’s phenotypic screening approach, traditionally used for antimicrobial discovery, is currently undergoing a renaissance for other therapeutic areas …”

Section: Discussionmentioning

confidence: 99%

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Metal Complexes as Antifungals? From a Crowd-Sourced Compound Library to the First In Vivo Experiments

Frei

Elliott

Kan

et al. 2022

JACS Au

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There are currently fewer than 10 antifungal drugs in clinical development, but new fungal strains that are resistant to most current antifungals are spreading rapidly across the world. To prevent a second resistance crisis, new classes of antifungal drugs are urgently needed. Metal complexes have proven to be promising candidates for novel antibiotics, but so far, few compounds have been explored for their potential application as antifungal agents. In this work, we report the evaluation of 1039 metal-containing compounds that were screened by the Community for Open Antimicrobial Drug Discovery (CO-ADD). We show that 20.9% of all metal compounds tested have antimicrobial activity against two representative Candida and Cryptococcus strains compared with only 1.1% of the >300,000 purely organic molecules tested through CO-ADD. We identified 90 metal compounds (8.7%) that show antifungal activity while not displaying any cytotoxicity against mammalian cell lines or hemolytic properties at similar concentrations. The structures of 21 metal complexes that display high antifungal activity (MIC ≤1.25 μM) are discussed and evaluated further against a broad panel of yeasts. Most of these have not been previously tested for antifungal activity. Eleven of these metal complexes were tested for toxicity in the Galleria mellonella moth larva model, revealing that only one compound showed signs of toxicity at the highest injected concentration. Lastly, we demonstrated that the organo-Pt(II) cyclooctadiene complex Pt1 significantly reduces fungal load in an in vivo G. mellonella infection model. These findings showcase that the structural and chemical diversity of metal-based compounds can be an invaluable tool in the development of new drugs against infectious diseases.

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Section: ■ Conclusionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Metal Complexes as Antifungals? From a Crowd-Sourced Compound Library to the First In Vivo Experiments

Frei

Elliott

Kan

et al. 2022

JACS Au

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“…It is generally not possible to easily accomplish either of these goals with single target-based approaches, as extensive knowledge of the target is needed to maximize the probability of success and for biomarker purposes. Because of this, it has been argued that successful target-based approaches have often been dependent on previous phenotypic screening for identification and development of first-in-class compounds, and that phenotypic screening is an important complementary approach to target-based methods ( Moffat et al., 2017 ; Swinney, 2013 ; Vincent et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…Because of this, it has been argued that successful target-based approaches have often been dependent on previous phenotypic screening for identification and development of first-in-class compounds, and that phenotypic screening is an important complementary approach to target-based methods (Moffat et al, 2017;Swinney, 2013;Vincent et al, 2022).…”

Section: Llmentioning

confidence: 99%

A tubulin binding molecule drives differentiation of acute myeloid leukemia cells

et al. 2022

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“…The multiple interactions between drugs and biological targets might well underlie unwanted side-effects, but often they also contribute to therapeutical efficacy (Vincent et al, 2022). In particular, even mild modulation of several targets simultaneously can be much more efficient than strong modulation of a single target.…”

Section: Target-based Vs Phenotypic Drug Discoverymentioning

confidence: 99%

How artificial intelligence enables modeling and simulation of biological networks to accelerate drug discovery

DiNuzzo¹

2022

Front. Drug. Discov.

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The pharmaceutical industry suffered a significant decline of innovation in the last few decades, whose simple reason is complex biology. Artificial intelligence (AI) promises to make the entire drug discovery and development process more efficient. Here I consider the potential benefits of using AI to deepen our mechanistic understanding of disease by leveraging data and knowledge for modeling and simulation of genome-scale biological networks. I outline recent developments that are moving the field forward and I identify several overarching challenges for advancing the state of the art towards the successful integration of AI with modeling and simulation in drug discovery.

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Phenotypic drug discovery: recent successes, lessons learned and new directions

Cited by 101 publications

References 219 publications

Metal Complexes as Antifungals? From a Crowd-Sourced Compound Library to the First In Vivo Experiments

Metal Complexes as Antifungals? From a Crowd-Sourced Compound Library to the First In Vivo Experiments

A tubulin binding molecule drives differentiation of acute myeloid leukemia cells

How artificial intelligence enables modeling and simulation of biological networks to accelerate drug discovery

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