Predictive Toxicology Approaches for Small Molecule Oncology Drugs

Maziasz, Timothy J.; Kadambi, Vivek J.; Silverman, Lee; Fedyk, Eric R.; Alden, Carl L.

doi:10.1177/0192623309356448

Cited by 18 publications

(8 citation statements)

References 62 publications

(72 reference statements)

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“…1–4 It is not uncommon for candidates to pass this screening process successfully only to exhibit poor specificity, solubility and distribution in vivo . 5–9 Such failures, deep in the clinical testing process, results in tremendous research and development costs that are ultimately passed on to consumers through the limited number of drugs that do make it to the market. It is possible to lower the costs of development for promising drug agents to the clinical trial phase by creating drug formulations that improve efficacy and reduce side effects through targeted delivery.…”

Section: Introductionmentioning

confidence: 99%

New surface radiolabeling schemes of super paramagnetic iron oxide nanoparticles (SPIONs) for biodistribution studies

et al. 2015

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Nanomaterial based drug delivery systems allow for the independent tuning of the surface chemical and physical properties that affect their biodistribution in vivo and the therapeutic payloads that they are intended to deliver. Additionally, the added therapeutic and diagnostic value of their inherent material properties often provides extra functionality. Iron based nanomaterials with their magnetic properties and easily tailorable surface chemistry are of particular interest as model systems. In this study the core radius of the iron oxide nanoparticles (NPs) was 14.08 ± 3.92 nm while the hydrodynamic radius of the NPs, as determined by Dynamic Light Scattering (DLS), was between 90–110 nm. In this study, different approaches were explored to create radiolabeled NPs that are stable in solution. The NPs were functionalized with polycarboxylate or polyamine surface functional groups. Polycarboxylate functionalized NPs had a zeta potential of –35 mV and polyamine functionalized NPs had a zeta potential of +40 mV. The polycarboxylate functionalized NPs were chosen for in vivo biodistribution studies and hence were radiolabeled with 14C, with a final activity of 0.097 nCi mg–1 of NPs. In chronic studies, the biodistribution profile is tracked using low-level radiolabeled proxies of the nanoparticles of interest. Conventionally, these radiolabeled proxies are chemically similar but not chemically identical to the non-radiolabeled NPs of interest. This study is novel as different approaches were explored to create radiolabeled NPs that are stable, possess a hydrodynamic radius of <100 nm and most importantly they exhibit an identical surface chemical functionality as their non-radiolabeled counterparts. Identical chemical functionality of the radiolabeled probes to the non-radiolabeled probes was an important consideration to generate statistically similar biodistribution data sets using multiple imaging and detection techniques. The radiolabeling approach described here is applicable to the synthesis of a large class of nanomaterials with multiple core and surface functionalities. This work combined with the biodistribution data suggests that the radiolabeling schemes carried out in this study have broad implications for use in pharmacokinetic studies for a variety of nanomaterials.

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Section: Introductionmentioning

confidence: 99%

New surface radiolabeling schemes of super paramagnetic iron oxide nanoparticles (SPIONs) for biodistribution studies

et al. 2015

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“…In general, these filters are based on empirical rules targeting several pharmacokinetic indices that confer crucial information for the speed and success in drug discovery. Herein, we predicted for carvacrol thirteen of the most crucial drug-likeness filters recommended during the design of cancer drug candidates [ 35 , 36 ], which are shown in Table 4 .…”

Section: Resultsmentioning

confidence: 99%

Carvacrol: An In Silico Approach of a Candidate Drug on HER2, PI3Kα, mTOR, hER‐α, PR, and EGFR Receptors in the Breast Cancer

Herrera-Calderon

Yepes-Pérez

Quintero‐Saumeth

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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Carvacrol is a phenol monoterpene found in aromatic plants specially in Lamiaceae family, which has been evaluated in an experimental model of breast cancer. However, any proposed mechanism based on its antitumor effect has not been reported. In our previous study, carvacrol showed a protective effect on 7,12-dimethylbenz[α]anthracene- (DMBA-) induced breast cancer in female rats. The main objective in this research was to evaluate by using in silico study the carvacrol on HER2, PI3Kα, mTOR, hER-α, PR, and EGFR receptors involved in breast cancer progression by docking analysis, molecular dynamic, and drug-likeness evaluation. A multilevel computational study to evaluate the antitumor potential of carvacrol focusing on the main targets involved in the breast cancer was carried out. The in silico study starts with protein-ligand docking of carvacrol followed by ligand pathway calculations, molecular dynamic simulations, and molecular mechanics energies combined with the Poisson–Boltzmann (MM/PBSA) calculation of the free energy of binding for carvacrol. As result, the in silico study led to the identification of carvacrol with strong binding affinity on mTOR receptor. Additionally, in silico drug-likeness index for carvacrol showed a good predicted therapeutic profile of druggability. Our findings suggest that mTOR signaling pathway could be responsible for its preventive effect in the breast cancer.

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“…In an attempt to evaluate different drug discovery strategies, it was observed that the percentage of newly approved small molecule drugs with a novel molecular mechanism of action is less than 20 % of the total approvals during the study duration considered [ 2 ]. Currently, the majority of drug candidates are aimed at cancer treatment and are therefore studied for activity at multiple, possibly novel biological targets, presenting a high probability of multiple unique toxicological profiles [ 3 ]. Therefore, it is essential to employ novel strategies that can predict the fate of the chemicals in early stages of development to overcome the failure rates and accelerate the development and approval of promising candidates.…”

Section: Introductionmentioning

confidence: 99%

Computational methods for prediction of in vitro effects of new chemical structures

et al. 2016

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BackgroundWith a constant increase in the number of new chemicals synthesized every year, it becomes important to employ the most reliable and fast in silico screening methods to predict their safety and activity profiles. In recent years, in silico prediction methods received great attention in an attempt to reduce animal experiments for the evaluation of various toxicological endpoints, complementing the theme of replace, reduce and refine. Various computational approaches have been proposed for the prediction of compound toxicity ranging from quantitative structure activity relationship modeling to molecular similarity-based methods and machine learning. Within the “Toxicology in the 21st Century” screening initiative, a crowd-sourcing platform was established for the development and validation of computational models to predict the interference of chemical compounds with nuclear receptor and stress response pathways based on a training set containing more than 10,000 compounds tested in high-throughput screening assays.ResultsHere, we present the results of various molecular similarity-based and machine-learning based methods over an independent evaluation set containing 647 compounds as provided by the Tox21 Data Challenge 2014. It was observed that the Random Forest approach based on MACCS molecular fingerprints and a subset of 13 molecular descriptors selected based on statistical and literature analysis performed best in terms of the area under the receiver operating characteristic curve values. Further, we compared the individual and combined performance of different methods. In retrospect, we also discuss the reasons behind the superior performance of an ensemble approach, combining a similarity search method with the Random Forest algorithm, compared to individual methods while explaining the intrinsic limitations of the latter.ConclusionsOur results suggest that, although prediction methods were optimized individually for each modelled target, an ensemble of similarity and machine-learning approaches provides promising performance indicating its broad applicability in toxicity prediction.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0162-2) contains supplementary material, which is available to authorized users.

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Predictive Toxicology Approaches for Small Molecule Oncology Drugs

Cited by 18 publications

References 62 publications

New surface radiolabeling schemes of super paramagnetic iron oxide nanoparticles (SPIONs) for biodistribution studies

New surface radiolabeling schemes of super paramagnetic iron oxide nanoparticles (SPIONs) for biodistribution studies

Carvacrol: An In Silico Approach of a Candidate Drug on HER2, PI3Kα, mTOR, hER‐α, PR, and EGFR Receptors in the Breast Cancer

Computational methods for prediction of in vitro effects of new chemical structures

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