Physiologic Targets and Modes of Action for CBL0137, a Lead for Human African Trypanosomiasis Drug Development

Sanz-Rodriguez, Carlos E.; Hoffman, Benjamin U.; Guyett, Paul J.; Purmal, Andrei A.; Singh, Baljinder; Pollastri, Michael P.; Mensa-Wilmot, Kojo

doi:10.1124/molpharm.121.000430

Cited by 4 publications

(4 citation statements)

References 90 publications

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“…As a metric for the irreversible killing of trypanosomes, the cidality of 5v was determined after a short-term (6 h) exposure of cells to the hit. 8 The concentration of 5v that caused 50% trypanocidality (DCC 50 ) was 160 nM, and 90% cidality (DCC 90 ) required 870 nM of the compound ( Figure 3 ). For 5w , DCC 50 and DCC 90 were 690 nM and 5.6 μM, respectively.…”

Section: Resultsmentioning

confidence: 99%

Chemical Optimization of CBL0137 for Human African Trypanosomiasis Lead Drug Discovery

et al. 2023

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The carbazole CBL0137 (1) is a lead for drug development against human African trypanosomiasis (HAT), a disease caused by Trypanosoma brucei. To advance 1 as a candidate drug, we synthesized new analogs that were evaluated for the physicochemical properties, antitrypanosome potency, selectivity against human cells, metabolism in microsomes or hepatocytes, and efflux ratios. Structure–activity/property analyses of analogs revealed eight new compounds with higher or equivalent selectivity indices (5j, 5t, 5v, 5w, 5y, 8d, 13i, and 22e). Based on the overall compound profiles, compounds 5v and 5w were selected for assessment in a mouse model of HAT; while 5v demonstrated a lead-like profile for HAT drug development, 5w showed a lack of efficacy. Lessons from these studies will inform further optimization of carbazoles for HAT and other indications.

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Section: Resultsmentioning

confidence: 99%

Chemical Optimization of CBL0137 for Human African Trypanosomiasis Lead Drug Discovery

et al. 2023

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“…Many drug-binding proteins are not physiological targets which may be defined as proteins whose genetic disruption yields similar modes of action as the treatment of cells with the drug ( Mensa-Wilmot, 2021 ). Recent efforts to address the topic systematically with multi-disciplinary experimental approaches hold promise ( Meyer and Shapiro, 2021 ; Sanz-Rodriguez et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…Two experimental strategies can be used to obtain mode of action data. First, one could identify drug-binding proteins, and from that list predict modes of a drug’s action ( Figure 10 ), as recently demonstrated for CBL0137 ( Sanz-Rodriguez et al., 2022 ; Thomas et al., 2016 ). Second, one could perturb cells with a drug and after proteome analysis to identify destabilized proteins ( i.e., drug perturbome ( Table 1 ), and predict modes of drug action from roles of destabilized proteins ( Figure 10 ).…”

Section: Discussionmentioning

confidence: 99%

“…This second strategy can accelerate the determination of modes of action because of not requiring prior knowledge of drug-binding proteins ( Figure 10 ). Data from modes of action studies are compared to molecular effects obtained from the genetic disruption of drug-binding proteins to identify physiologic targets from drug-binding proteins ( Jones et al., 2015 ; Sanz-Rodriguez et al., 2022 ; Wall et al., 2018 ) ( Figure 10 ).

Figure 10 Contributions of data from cell perturbome proteomics and drug binding proteins to the identification of physiologic drug targets …”

Section: Discussionmentioning

confidence: 99%

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Hypothesis-generating proteome perturbation to identify NEU-4438 and acoziborole modes of action in the African Trypanosome

Sharma¹,

Cipriano²,

Ferrins³

et al. 2022

iScience

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Predicting anti-trypanosome effect of carbazole-derived compounds by powerful SVM with novel kernel function and comprehensive learning PSO

Dong,

Zhang

2024

Antimicrob Agents Chemother

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In order to predict the anti-trypanosome effect of carbazole-derived compounds by quantitative structure–activity relationship, five models were established by the linear method, random forest, radial basis kernel function support vector machine, linear combination mix-kernel function support vector machine, and nonlinear combination mix-kernel function support vector machine (NLMIX-SVM). The heuristic method and optimized CatBoost were used to select two different key descriptor sets for building linear and nonlinear models, respectively. Hyperparameters in all nonlinear models were optimized by comprehensive learning particle swarm optimization with low complexity and fast convergence. Furthermore, the models’ robustness and reliability underwent rigorous assessment using fivefold and leave-one-out cross-validation, y -randomization, and statistics including concordance correlation coefficient (CCC), Q F 1 2 , Q F 2 2 , and Q F 3 2 . Among all the models, the NLMIX-SVM model, which was established by support vector regression using a nonlinear combination of radial basis kernel function, sigmoid kernel function, and linear kernel function as a new kernel function, demonstrated excellent learning and generalization abilities as well as robustness: R 2 = 0.9581, mean square error (MSE) = 0.0199 for the training set and R 2 = 0.9528, MSE = 0.0174 for the test set. Q L O O 2 , Q 5 − f o l d 2 , CCC, Q F 1 2 , Q F 2 2 , and Q F 3 2 are 0.9539, 0.8908, 0.9752, 0.9529, 0.9528, and 0.9633, respectively. The NLMIX-SVM method proved to be a promising way in quantitative structure–activity relationship research. In addition, molecular docking experiments were conducted to analyze the properties of new derivatives, and a new potential candidate drug molecule was ultimately found. In summary, this study will provide help for the design and screening of novel anti-trypanosome drugs.

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Physiologic Targets and Modes of Action for CBL0137, a Lead for Human African Trypanosomiasis Drug Development

Cited by 4 publications

References 90 publications

Chemical Optimization of CBL0137 for Human African Trypanosomiasis Lead Drug Discovery

Chemical Optimization of CBL0137 for Human African Trypanosomiasis Lead Drug Discovery

Hypothesis-generating proteome perturbation to identify NEU-4438 and acoziborole modes of action in the African Trypanosome

Predicting anti-trypanosome effect of carbazole-derived compounds by powerful SVM with novel kernel function and comprehensive learning PSO

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