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<p>The simultaneous quantification of active pharmaceutical ingredients alongside their mutagenic impurities represents a critical challenge in pharmaceutical quality control. This study presents the first multicolor analytical platform for concurrent determination of bisoprolol fumarate (BIP), amlodipine besylate (AML), and 4-hydroxybenzaldehyde (HBZ), a Class 3 mutagenic impurity in BIP requiring strict regulatory monitoring. Two complementary methodologies were developed: high-performance thin-layer chromatography (HPTLC)-densitometry and Firefly Algorithm-optimized partial least squares (FA-PLS) spectrophotometry, both aligned with green analytical chemistry (GAC) and white analytical chemistry (WAC) principles. The HPTLC method employed an eco-friendly mobile phase of ethyl acetate–ethanol (7:3, v/v), achieving baseline separation with Rf values of 0.29 ± 0.02 (HBZ), 0.72 ± 0.01 (AML), and 0.83 ± 0.01 (BIP). The FA-PLS model incorporated a novel Hammersley Sequence Sampling (HSS) strategy for validation set construction, ensuring uniform concentration space coverage and eliminating sampling bias inherent in conventional random approaches. This innovation, combined with a 5<sup>2</sup> mixture experimental design for calibration (25 mixtures), significantly enhanced model robustness and predictive capability. Both methods demonstrated superior analytical performance with detection limits of 3.56–20.52 ng/band (HPTLC) and 0.011–0.120 μg/mL (FA-PLS), correlation coefficients ≥ 0.9995, and precision (RSD) ≤ 2%. Comprehensive sustainability assessment using multiple evaluation tools revealed exceptional environmental profiles: perfect NEMI, AGREE, and ComplexGAPI scores, high GEMAM indices (7.015 and 7.487), minimal carbon footprints (0.037 and 0.021 kg CO₂/sample), and outstanding BAGI (87.50 and 90.00), VIGI (75.00 and 80.00), and RGBfast scores (81.00 and 85.00) for HPTLC and FA-PLS, respectively. NQS evaluation confirmed alignment with eleven UN Sustainable Development Goals, particularly SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production), yielding overall sustainability scores of 82% and 83%. Successful application to pharmaceutical dosage forms validated the methods' practical utility. This work establishes a new paradigm in sustainable pharmaceutical analysis, demonstrating how algorithmic optimization and environmental consciousness can synergistically advance analytical science while meeting stringent regulatory requirements.</p>

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Dual-platform integration of HPTLC and firefly algorithm-optimized chemometrics with hammersley sequence sampling for simultaneous quantification of bisoprolol, amlodipine, and mutagenic impurity 4-hydroxybenzaldehyde

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月12日

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  • Lateefa A. Al-Khateeb,
  • Ahmed Emad F. Abbas,
  • Mohamed R. Elghobashy,
  • Nisreen F. Abo Talib,
  • Ibrahim A. Naguib,
  • Mohammed Alqarni,
  • Michael K. Halim

摘要

The simultaneous quantification of active pharmaceutical ingredients alongside their mutagenic impurities represents a critical challenge in pharmaceutical quality control. This study presents the first multicolor analytical platform for concurrent determination of bisoprolol fumarate (BIP), amlodipine besylate (AML), and 4-hydroxybenzaldehyde (HBZ), a Class 3 mutagenic impurity in BIP requiring strict regulatory monitoring. Two complementary methodologies were developed: high-performance thin-layer chromatography (HPTLC)-densitometry and Firefly Algorithm-optimized partial least squares (FA-PLS) spectrophotometry, both aligned with green analytical chemistry (GAC) and white analytical chemistry (WAC) principles. The HPTLC method employed an eco-friendly mobile phase of ethyl acetate–ethanol (7:3, v/v), achieving baseline separation with Rf values of 0.29 ± 0.02 (HBZ), 0.72 ± 0.01 (AML), and 0.83 ± 0.01 (BIP). The FA-PLS model incorporated a novel Hammersley Sequence Sampling (HSS) strategy for validation set construction, ensuring uniform concentration space coverage and eliminating sampling bias inherent in conventional random approaches. This innovation, combined with a 52 mixture experimental design for calibration (25 mixtures), significantly enhanced model robustness and predictive capability. Both methods demonstrated superior analytical performance with detection limits of 3.56–20.52 ng/band (HPTLC) and 0.011–0.120 μg/mL (FA-PLS), correlation coefficients ≥ 0.9995, and precision (RSD) ≤ 2%. Comprehensive sustainability assessment using multiple evaluation tools revealed exceptional environmental profiles: perfect NEMI, AGREE, and ComplexGAPI scores, high GEMAM indices (7.015 and 7.487), minimal carbon footprints (0.037 and 0.021 kg CO₂/sample), and outstanding BAGI (87.50 and 90.00), VIGI (75.00 and 80.00), and RGBfast scores (81.00 and 85.00) for HPTLC and FA-PLS, respectively. NQS evaluation confirmed alignment with eleven UN Sustainable Development Goals, particularly SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production), yielding overall sustainability scores of 82% and 83%. Successful application to pharmaceutical dosage forms validated the methods' practical utility. This work establishes a new paradigm in sustainable pharmaceutical analysis, demonstrating how algorithmic optimization and environmental consciousness can synergistically advance analytical science while meeting stringent regulatory requirements.