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Assessment of computational prediction of infra-red spectrograms of nucleotide reverse transcriptase inhibitor class antiretroviral drugs in the FT-IR method validation

M. Malarvannan, G. Chiranjeevi, Vinod Kumar Kondreddy, Suyadevara Punna Rao, and Rufus Amalan Robert

Department of Pharmaceutical Analysis, Raghavendra Institute of Pharmaceutical Education and Research (RIPER)-Autonomous, Anantapur, India



Received: 11 November 2022  Accepted: 22 July 2023


The research findings involving computational chemistry modelling and evaluation using experimental data are challenging. The results may be verified by the computational predictions at an early stage of the research process. In the discipline of analytical chemistry, a number of Insilco techniques have lately been developed; however, their application is difficult and relatively limited. Increasing their use may protect researchers from being misled. In this study, the percentage error and deviation were estimated while examining the reference and experimental data of chemical compounds from the nucleotide reverse transcriptase inhibitor (NRTIs) class of antiretroviral drugs. The motivation behind this study to utilize the current computational software applications for the analytical developments such as pre and post developments. The molecular geometries calculated using DFT were compared to the experimental data already available. The FT-IR spectra of the compounds with the title were collected in the 4000–400 cm1 range. The potential energy distribution (PED) was used for predicting each vibrational frequency and its associated vibrational modes. The experimental IR shifts upon comparison with predicted spectrum frequencies, as well as the PED findings and our reassignments. Our research indicates that for the majority of bands that are associated with other elements, such as asymmetric and symmetric NH, CH stretching and in-plane deformations, CH3, rocking vibrations, the PED contributions, Gauss View animation, and observed shifts exhibit comparable results. Finally, we developed the FT-IR method validation for Lamivudine by combining the calculation prediction of infrared spectrums with the experimental data (LAM). The method obeys Beer’s law, the concentration range of 1 mg/ml to 3 mg/ml. The regression equation was found to be considering concentration versus absorbance, with y = 0.1414× + 0.112 being 0.9996 and y = 0.1674× + 0.0732 being 0.9849 against Log Pb-Log Po values. As per ICH guideline Q2 (R1), this technique was validated using a number of parameters, including a limit of detection (LOD), limit of quantification (LOQ), precision, accuracy, and robustness. Almost all of these parameters had an RSD of less than 2%. The final assessment also produced reliable results.

Keywords: Infrared spectrum prediction; KBr pressed pellet method; FT-IR method validation; Lamivudine (LAM)

Full paper is available at

DOI: 10.1007/s11696-023-03004-x


Chemical Papers 77 (11) 7131–7154 (2023)

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