Synthesis, Biological Evaluation, and QSAR Studies of 3-Iodochromone Derivatives as Potential Fungicides

Abstract

Despite the emergence of novel biotechnological and biological solutions, agrochemicals continue to play an important role in crop protection. Fungicide resistance is becoming a major problem; numerous cases of fungicide resistance have occurred worldwide in the last decade, resulting in the loss of several fungicides. The discovery of new molecules has therefore assumed critical importance in crop protection. In our quest for biologically active molecules, we herein report the synthesis of a series of twenty-one 3-Iodochromone derivatives (4a–4u), in a two-step process by condensation of 2-hydroxyacetophenone derivatives (2a–2u) with N,N-dimethylformamidedimethylacetal yielding enaminones (3a–3u), followed by cyclization with iodine to corresponding 3-iodochromones. Characterization of these compounds was done by IR, 1H NMR, 13C NMR, and LC-HRMS techniques. All synthesized compounds were screened for their fungicidal activity against Sclerotium rolfsii. Among these 6,8-Dichloro-3-iodochromone 4r was found to be most active (ED50 = 8.43 mg L−1). 2D-Quantitative Structural Activity Relationship (2D-QSAR) analysis was also performed by generating three different models viz., Multiple Linear Regression (MLR, Model 1), Principal Component Regression (PCR, Model 2), and Partial Least Squares (PLS, Model 3). Predictive power and statistical significance of these models were assessed with external and internal validation and leave one-out cross-validation was used for verification. In QSAR study, MLR (Model 1) was found to be best having correlation coefficient (r2) 0.943, cross-validated correlation coefficient (q2) 0.911 and r2pred 0.837. It was observed that DeltaEpsilonC, T_2_Cl_6, T_2_F_6, T_T_F_3, and ZCompDipole are the major descriptors which influence the fungicidal activity of 3-Iodochromone derivatives. The physicochemical parameters were estimated by the VLifeMDS 4.6 software. The QSAR study results will be helpful for structure optimization to improve the activity.

PIC50: An open source tool for interconversion of PIC50values and IC50for efficient data representation and analysis

“…This error will be automatically be carried forward in the whole QSAR model generation and will affect the overall accuracy of the QSAR model significantly. In another case study, a total of twenty-one 3-Iodochrome derivatives were synthesized and the QSAR model was generated for potential fungicides (Kaushik et al, 2021). This study is a perfect example of incorrect data representation arising due to a lack of a proper tool to calculate PIC 50 values from the IC 50 values.…”

“…In another case study, a total of twenty-one 3-Iodochrome derivatives were synthesized and the QSAR model was generated for potential fungicides (Kaushik et al, 2021). This study is a perfect example of incorrect data representation arising due to a lack of a proper tool to calculate PIC 50 values from the IC 50 values.…”

“…This study is a perfect example of incorrect data representation arising due to a lack of a proper tool to calculate PIC 50 values from the IC 50 values. In this study, ED 50 values of the compounds were evaluated against Sclerotium rolfsii in the mgL -1 (Kaushik et al, 2021). For developing the QSAR model, the author utilized the pED 50 values, however, for these calculations authors did not consider the factor of the unit of measurement i.e.…”

“…Table 4 depicts the pED 50 value calculated by the author(Kaushik et al, 2021) and the pED 50 value calculated by our software from the given ED 50 .Considering the example of compound '4a' from the table, ED 50 was reported to be 50.38 mgL -1 .The authors computed the negative logarithm of ED 50 values directly of the compound '4a' without taking the 10 -3 (for milligram) into the calculation. Since pED 50 values are the negative logarithm of EC 50 values in molar concentration, the concentration factor has to be taken into consideration.…”

DataSheet1.pdf

“…The results of the anti-HSV-1 activity assay were affected by experimental conditions, such as incubation time or cell type. Regression-based methods are commonly used and successfully perform activity prediction (Nagai et al, 2019;Kaushik et al, 2021). However, because these methods provide concrete activity values as output, they may contain large errors when using data with inconsistent experimental protocols.…”