The current status in computational exploration of Pt(<scp>iv</scp>) prodrug activation by reduction

Ponte, Fortuna; Scoditti, Stefano; Mazzone, Gloria; Sicilia, Emilia

doi:10.1039/d3cp01150j

Cited by 8 publications

(6 citation statements)

References 82 publications

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“…Computational and experimental investigations highlighted that the electrochemical properties and, specifically, the propensity of Pt IV complexes to undergo two-electron reduction, measured by the reduction potential in outer-sphere reduction reactions, are determined by the nature of the axial and, although to a lesser extent, equatorial ligands. ,− By predicting the reduction potential, researchers can establish valuable structure–property relationships, facilitating the rational design of new complexes with tailored electrochemical characteristics. While conventional methods such as quantum-mechanical calculations, particularly DFT, have historically been the standard for predicting redox potentials, this study explores an innovative alternative strategy based on ML techniques, which can supply accurate predictions more efficiently.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Prediction of Reduction Potentials for Pt^IV Complexes

Vigna,

Cova,

Nunes

et al. 2024

J. Chem. Inf. Model.

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Some of the well-known drawbacks of clinically approved Pt II complexes can be overcome using six-coordinate Pt IV complexes as inert prodrugs, which release the corresponding fourcoordinate active Pt II species upon reduction by cellular reducing agents. Therefore, the key factor of Pt IV prodrug mechanism of action is their tendency to be reduced which, when the involved mechanism is of outer-sphere type, is measured by the value of the reduction potential. Machine learning (ML) models can be used to effectively capture intricate relationships within Pt IV complex data, leading to highly accurate predictions of reduction potentials and other properties, and offering significant insights into their electrochemical behavior and potential applications. In this study, a machine learning-based approach for predicting the reduction potentials of Pt IV complexes based on relevant molecular descriptors is presented. Leveraging a data set of experimentally determined reduction potentials and a diverse range of molecular descriptors, the proposed model demonstrates remarkable predictive accuracy (MSE = 0.016 V 2 , RMSE = 0.13 V, R 2 = 0.92). Ab initio calculations and a set of different machine learning algorithms and feature engineering techniques have been employed to systematically explore the relationship between molecular structure and similarity and reduction potential. Specifically, it has been investigated whether the reduction potential of these compounds can be described by combining ML models across different combinations of constitutional, topological, and electronic molecular descriptors. Our results not only provide insights into the crucial factors influencing reduction potentials but also offer a rapid and effective tool for the rational design of Pt IV complexes with tailored electrochemical properties for pharmaceutical applications. This approach has the potential to significantly expedite the development and screening of novel Pt IV prodrug candidates. The analysis of principal components and key features extracted from the model highlights the significance of structural descriptors of the 2D Atom Pairs type and the lowest unoccupied molecular orbital energy. Specifically, with just 20 appropriately selected descriptors, a notable separation of complexes based on their reduction potential value is achieved.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Prediction of Reduction Potentials for Pt^IV Complexes

Vigna,

Cova,

Nunes

et al. 2024

J. Chem. Inf. Model.

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“…Meanwhile axial chlorido and hydroxido ligands favor an inner-sphere mechanism. The reduction potential of Cl-bearing Pt( iv ) complexes is also usually higher, 35 favoring the reduction of such complexes.…”

Section: Physicochemical Properties Of Pt(iv) Prodrugsmentioning

confidence: 99%

Pt(iv) anticancer prodrugs bearing an oxaliplatin scaffold: what do we know about their bioactivity?

Lopez-Sanchez,

Bertrand

2024

Inorg. Chem. Front.

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“…The reduction of 2 by (Figure 5a) follows a ligand-bridged-H transfer mechanism as already observed for other reducing agents. 42 Two pathways were explored, each involving H-bonds between the N-H of LMB and either the carbonyl O or the ester O of the Pt-coordinate succinato ligand of 2. DFT calculations indicate that both pathways exhibit similar trends, although the one involving the formation of a bridge with the Ptbound O atom is preferred kinetically (12.7 vs 20.5 kcal•mol -1 ).…”

Section: Catalytic Mechanismmentioning

confidence: 99%

Red-Light Photocatalytic Activation Of Pt(IV) Anticancer Prodrugs Using Methylene Blue

Carrasco,

Bajetto,

Scoditti

et al. 2024

Preprint

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Catalysis-based approaches offer versatile strategies for activating anticancer prodrugs, potentially allowing precise control over drug release and localization within tumor tissues, while reducing systemic toxicity. In this study, we explore the role of the phenothiazine dye methylene blue (MB+) as a photocatalyst in conjunction with biologically relevant electron donors to facilitate the red-light conversion of two Pt(IV) complexes, denoted as cis,cis,trans-[PtCl2(NH3)2(O2CCH2CH2COOH)2] (1) and trans-[Pt(O2CCH2CH2COOH)21R,2R-(DACH)(ox)] (2), into cisplatin and oxaliplatin, respectively. Combining spectroscopic techniques (NMR, UV-Vis, flash photolysis) with computational methods, we reveal that the doubly reduced MB+ (leucomethylene blue, LMB) triggers the reductive elimination of axial ligands in the two Pt(IV) precursors, generating the corresponding Pt(II) anticancer drugs. In vitro experiments conducted on the human cervical cancer cell line CaSki, which harbors multiple copies of the integrated HPV-16 genome, and on non-tumoral cells (HaCat) demonstrate that co-administration with Pt(IV) prodrugs improves MB+'s antiproliferative efficacy in cancer cells, particularly under red light exposure. This enhancement arises from the catalytic production of Pt(II) species within the cellular environment.

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The current status in computational exploration of Pt(iv) prodrug activation by reduction

Abstract: Octahedral PtIV complexes are considered to be highly promising candidates for overcoming some shortcomings of clinically approved PtII drugs. PtIV compounds, owing to their inertia, appear to be able resisting...

Cited by 8 publications

References 82 publications

Machine Learning-Based Prediction of Reduction Potentials for Pt^IV Complexes

Machine Learning-Based Prediction of Reduction Potentials for Pt^IV Complexes

Pt(iv) anticancer prodrugs bearing an oxaliplatin scaffold: what do we know about their bioactivity?

Red-Light Photocatalytic Activation Of Pt(IV) Anticancer Prodrugs Using Methylene Blue

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The current status in computational exploration of Pt(iv) prodrug activation by reduction

Abstract: Octahedral PtIV complexes are considered to be highly promising candidates for overcoming some shortcomings of clinically approved PtII drugs. PtIV compounds, owing to their inertia, appear to be able resisting...

Cited by 8 publications

References 82 publications

Machine Learning-Based Prediction of Reduction Potentials for PtIV Complexes

Machine Learning-Based Prediction of Reduction Potentials for PtIV Complexes

Pt(iv) anticancer prodrugs bearing an oxaliplatin scaffold: what do we know about their bioactivity?

Red-Light Photocatalytic Activation Of Pt(IV) Anticancer Prodrugs Using Methylene Blue

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Product

Resources

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Machine Learning-Based Prediction of Reduction Potentials for Pt^IV Complexes

Machine Learning-Based Prediction of Reduction Potentials for Pt^IV Complexes