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Quantitative structure–activity relationship modeling of hydroxylated polychlorinated biphenyls as constitutive androstane receptor agonists

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Abstract

Hydroxylated polychlorinated biphenyls (OH-PCBs), a series of toxic chemical compounds produced via biotic and abiotic transformation of polychlorinated biphenyls (PCBs), are known to cause endocrine disruption by interacting inappropriately with human nuclear receptors. Due to occurrence of high numbers of inactive OH-PCB congeners recorded in many experimental toxicity studies, it is pertinent to develop rapid and inexpensive QSAR models that can reliably predict the activities of OH-PCB congeners prior to experimental testing. Using a combination of genetic function approximation and multiple linear regression methods, a local QSAR model, consisting of six 2D descriptors (MATS1s, VE3_DzZ, VE1_Dzp, SpMin8_Bhv, SpMax5_Bhi, topoRadius) and two 3D descriptors (RDF95u, RDF45m), was developed from a training set of 44 OH-PCBs. Statistical parameters for fitting (\({R}^{2}\) = 0.8902, \({R}_{adj}^{2}\) = 0.8651, s = 0.2840), cross-validation (\({Q}_{LOO}^{2}\) = 0.8201, \({RMSE}_{CV}\) = 0.3242), and Y-randomization (\({cR}_{p}^{2}\) = 0.8019) obtained for the developed QSAR model indicate that the model is reliable, robust, and provides good fit to the data in the training set. The results of external validation carried out on 20 OH-PCBs in the test set also indicate that the developed QSAR model possessed good external predictivity and can be used to predict the agonistic activities of untested OH-PCB congeners to constitutive androstane receptor.

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References

  1. Weikum ER, Liu X, Ortlund EA (2018) The nuclear receptor superfamily: a structural perspective. Protein Sci 27:1876–1892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Robinson-Rechavi M, Garcia HE, Laudet V (2003) The nuclear receptor superfamily. J Cell Sci 116:585–586

    Article  PubMed  Google Scholar 

  3. Huang P, Chandra V, Rastinejad F (2010) Structural overview of the nuclear receptor superfamily: insights into physiology and therapeutics. Annu Rev Physiol 72:247–272

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Weatherman RV, Fletterick RJ, Scanlan TS (1999) Nuclear-receptor ligands and ligand-binding domains. Annu Rev Biochem 68:559–581

    Article  CAS  PubMed  Google Scholar 

  5. Steinmetz ACU, Renaud JP, Moras D (2001) Binding of ligands and activation of transcription by nuclear receptors. Annu Rev Biophys Biomol Struct 30:329–359

    Article  CAS  PubMed  Google Scholar 

  6. Bain DL, Heneghan AF, Connaghan-Jones KD, Miura MT (2007) Nuclear receptor structure: implications for function. Annu Rev Physiol 69:201–220

    Article  CAS  PubMed  Google Scholar 

  7. Toporova L, Balaguer P (2020) Nuclear receptors are the major targets of endocrine disrupting chemicals. Mol Cell Endocrinol 502:110665

    Article  CAS  PubMed  Google Scholar 

  8. Caliman FA, Gavrilescu M (2009) Pharmaceuticals, personal care products and endocrine disrupting agents in the environment – a review. Clean: Soil, Air, Water 37:277–303

    CAS  Google Scholar 

  9. Zhang Z, Jia C, Hu Y, Sun L, Jiao J, Zhao L, Zhu D, Li J, Tian Y, Bai H, Li R, Hu J (2012) The estrogenic potential of salicylate esters and their possible risks in foods and cosmetics. Toxicol Lett 209(2):146–153

    Article  CAS  PubMed  Google Scholar 

  10. Muncke J (2009) Exposure of endocrine disrupting compounds via the food chain: is packaging a relevant source? Sci Total Environ 407(16):4549–4559

    Article  CAS  PubMed  Google Scholar 

  11. Koo HJ, Lee BM (2004) Estimated exposure to phthalates in cosmetics and risk assessment. J Toxicol Environ Health Part A 67:1901–1914

    Article  CAS  Google Scholar 

  12. Rudel RA, Camann DE, Spengler JD, Korn LR, Brody JG (2003) Phthalates, alkylphenols, pesticides, polybrominated diphenyl ethers, other endocrine-disrupting compounds in indoor air and dust. Environ Sci Technol 37:4543–4553

    Article  CAS  PubMed  Google Scholar 

  13. Darbre PD (2018) Overview of air pollution and endocrine disorders. Int J Gen Med 11:191–207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pojana G, Gomiero A, Jonkers N, Marcomini A (2007) Natural and synthetic endocrine disrupting compounds (EDCs) in water, sediment and biota of a coastal lagoon. Environ Int 33(7):929–936

    Article  CAS  PubMed  Google Scholar 

  15. Rhind SM, Kyle CE, Ruffie H, Calmettes E, Osprey M, Zhang ZL, Hamilton D, McKenzie C (2013) Short and long-term temporal changes in soil concentrations of selected endocrine disrupting compounds (EDCs) following single or multiple applications of sewage sludge to pastures. Environ Pollut 181:262–270

    Article  CAS  PubMed  Google Scholar 

  16. Archer E, Wolfaardt GM, van Wyk JH (2017) Pharmaceutical and personal care products (PPCPs) as endocrine disrupting contaminants (EDCs) in South African surface waters. Water SA 43:684–706

    Article  CAS  Google Scholar 

  17. Loffredo E, Senesi N (2006) Fate of anthropogenic organic pollutants in soils with emphasis on adsorption/desorption processes of endocrine disruptor compounds. Pure Appl Chem 78(5):947–961

    Article  CAS  Google Scholar 

  18. Zamkowska D, Karwacka A, Jurewicz J, Radwan M (2018) Environmental exposure to non-persistent endocrine disrupting chemicals and semen quality: an overview of the current epidemiological evidence. Int J Occup Med Environ Health 31(4):377–414

    PubMed  Google Scholar 

  19. Rehman S, Usman Z, Rehman S, AlDraihem M, Rehman N, Rehman I, Ahmad G (2018) Endocrine disrupting chemicals and impact on male reproductive health. Transl Androl Urol 7(3):490–503

    Article  PubMed  PubMed Central  Google Scholar 

  20. Rahban R, Nef S (2020) Regional difference in semen quality of young men: a review on the implication of environmental and lifestyle factors during fetal life and adulthood. Basic Clin Androl 30:16

    Article  PubMed  PubMed Central  Google Scholar 

  21. Donato MD, Cernera G, Giovannelli P, Galasso G, Bilancio A, Migliaccio A, Castoria G (2017) Recent advances on bisphenol-A and endocrine disruptor effects on human prostate cancer. Mol Cell Endocrinol 457:35–42

    Article  PubMed  Google Scholar 

  22. Lacouture A, Lafront C, Peillex C, Pelletier M, Audet-Walsh E (2022) Impacts of endocrine-disrupting chemicals on prostate function and cancer. Environ Res 204:112085

    Article  CAS  PubMed  Google Scholar 

  23. Singh VK, Pal R, Srivastava P, Mistra G, Shukla Y, Sharma PK (2021) Exposure of androgen mimicking environmental chemicals enhances proliferation of prostate cancer (LNCaP) cells by inducing AR expression and epigenetic modification. Environ Pollut 272:116397

    Article  CAS  PubMed  Google Scholar 

  24. Hess-Wilson JK, Knudsen KE (2006) Endocrine disrupting compounds and prostate cancer. Cancer Lett 241:1–12

    Article  CAS  PubMed  Google Scholar 

  25. Fernandez MF, Olea N (2012) In: Diamanti-Kandarakis E and Gore AC (ed) Endocrine disruptors and puberty. Humana Press, New York

  26. Spinder N, Bergman JEH, van Tongeren M, Boezen HM, Kromhout H, de Walle HEK (2022) Maternal occupational exposure to endocrine-disrupting chemicals and urogenital anomalies in the offspring. Hum Reprod 37(1):142–151

    Article  CAS  Google Scholar 

  27. Yum T, Lee S, Kim Y (2013) Association between precocius puberty and some endocrine disruptors in human plasma. J Environ Sci Health A 48:912–917

    Article  CAS  Google Scholar 

  28. Lucaccioni L, Trevisani V, Marrozzini L, Bertoncelli N, Predieri B, Lugli L, Berardi A, Lughetti L (2020) Endocrine-disrupting chemicals and their effects during female puberty: a review of current evidence. Int J Mol Sci 21:2078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Srilanchakon K, Thadsri T, Jantarat C, Thengyai S, Nosoognoen W, Supornsilchai V (2017) Higher phthalate concentrations are associated with precocious puberty in normal weight Thai girls. J Pediatr Endocrinol Metab 30:1293–1298

    Article  CAS  PubMed  Google Scholar 

  30. Varnell RR, Arnold TJ, Quandt SA, Talton JW, Chen H, Miles CM, Daniel SS, Sandberg JC, Anderson KA, Arcury TA (2021) Menstrual cycle patterns and irregularities in hired Latinx child farmworkers. J Occup Environ Med 63(1):38–43

    Article  PubMed  PubMed Central  Google Scholar 

  31. Grindler NM, Allsworth JE, Macones GA, Kannan K, Roehl KA, Cooper AR (2015) Persistent organic pollutants and early menopause in U.S. women. PLoS One 10(1):e0116057

  32. Scsukova S, Rollerova E, Mlynarcikova AB (2016) Impact of endocrine disrupting chemicals on the onset and development of female reproductive disorders and hormone-related cancer. Reprod Biol 16(4):243–254

    Article  PubMed  Google Scholar 

  33. Wam MLY, Co VA, El-Nezami H (2021) Endocrine disrupting chemicals and breast cancer: a systematic review of epidemiological studies. Crit Rev Food Sci Nutr. https://doi.org/10.1080/10408398.2021.1903382

    Article  Google Scholar 

  34. Montes-Grajales D, Bernardes GJL, Olivero-Verbel JT (2016) Urban endocrine disruptors targeting breast cancer proteins. Chem Res Toxicol 29(2):150–161

    Article  CAS  PubMed  Google Scholar 

  35. Ben-Jonathan N (2019) In: Zhang X (ed) Estrogen receptor and breast cancer: celebrating the 60th anniversary of the discovery of ER. Humana Press, Switzerland

  36. Rogers JA, Metz L, Yong VW (2013) Review: endocrine disrupting chemicals and immune responses: a focus on bisphenol-A and its potential mechanisms. Mol Immunol 53:421–430

    Article  CAS  PubMed  Google Scholar 

  37. Kuo CH, Yang SN, Kuo PL, Hung CH (2012) Immunomodulatory effects of environmental endocrine disrupting chemicals. Kaohsiung J Med Sci 28:S37–S42

    Article  CAS  PubMed  Google Scholar 

  38. Dietert RR (2015) In: Darbre PD (ed) Endocrine disruption and human health. Elsevier, Amsterdam

  39. Sakkiah S, Wang T, Zou W, Wang Y, Pan B, Tong W, Hong H (2018) Endocrine disrupting chemicals mediated through binding androgen receptor are associated with diabetes mellitus. Int J Environ Res Public Health 15:25

    Article  Google Scholar 

  40. Lind PM, Lind L (2018) Endocrine-disrupting chemicals and risk of diabetes: an evidence-based review. Diabetologia 61:1495–1502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Alonso-Magdalena P, Quesada I, Nadal A (2011) Endocrine disruptors in the etiology of type 2 diabetes mellitus. Nat Rev Endocrinol 7(6):346–353

    Article  CAS  PubMed  Google Scholar 

  42. Ehrlich S, Lambers D, Baccarelli A, Khoury J, Macaluso M, Ho SM (2016) Endocrine disruptors: a potential risk factor for gestational diabetes mellitus. Am J Perinatol 33(13):1313–1318

    Article  PubMed  Google Scholar 

  43. Darbre PD (2015) In: Darbre PD (ed) Endocrine disruption and human health. Elsevier, Amsterdam

  44. Newbold RR, Padilla-Banks E, Jefferson WN, Heindel JJ (2008) Effects of endocrine disruptors on obesity. Int J Androl 31:201–208

    Article  CAS  PubMed  Google Scholar 

  45. Kim JT, Lee HK (2017) Childhood obesity and endocrine disrupting chemicals. Ann Pediatr Endocrinol Metab 22:219–225

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hatch EE, Nelson JW, Stahlhut RW, Webster TF (2010) Association of endocrine disruptors and obesity: perspectives from epidemiological studies. Int J Androl 33:324–332

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fu X, Xu J, Zhang R, Yu J (2020) The association between environmental endocrine disruptors and cardiovascular diseases: a systematic review and meta-analysis. Environ Res 187:109464

    Article  CAS  PubMed  Google Scholar 

  48. Tehrani R, Aken BV (2014) Hydroxylated polychlorinated biphenyls in the environment: sources, fate, and toxicity. Environ Sci Pollut Res Int 21:6334–6345

    Article  CAS  PubMed  Google Scholar 

  49. Takeuchi S, Shiraishi F, Kitamura S, Kuroki H, Jin K, Kojima H (2011) Characterization of steroid hormone receptor activities in 100 hydroxylated polychlorinated biphenyls, including congeners identified in humans. Toxicology 289:112–121

    Article  CAS  PubMed  Google Scholar 

  50. Kamata R, Shiraishi F, Kageyama S, Nakajima D (2015) Detection and measurement of the agonistic activities of PCBs and mono-hydroxylated PCBs to the constitutive androstane receptor using a recombinant yeast assay. Toxicol in Vitro 29:1859–1867

    Article  CAS  PubMed  Google Scholar 

  51. Kamata R, Nakajima D, Shiraishi F (2019) Measurement of the agonistic activities of monohydroxylated polychlorinated biphenyls at the retinoid X and retinoic acid receptors using recombinant yeast cells. Toxicol in Vitro 57:9–17

    Article  CAS  PubMed  Google Scholar 

  52. Cao LY, Ren XM, Guo LH (2019) Estrogen-related receptor γ is a novel target for lower-chlorinated polychlorinated biphenyls and their hydroxylated and sulfated metabolites. Environ Pollut 254:113088

    Article  CAS  PubMed  Google Scholar 

  53. Kitamura S, Jinno N, Suzuki T, Sugihara K, Ohta S, Kuroki H, Fujimoto N (2005) Thyroid hormone-like and estrogenic activity of hydroxylated PCBs in cell culture. Toxicology 208:377–387

    Article  CAS  PubMed  Google Scholar 

  54. Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34:854–866

    Article  CAS  Google Scholar 

  55. Kublbeck K, Niskanen J, Honkakoski P (2020) Metabolism-disrupting chemicals and the constitutive androstane receptor CAR. Cells 9:2306

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wang J, Hou T (2009) Recent advances on in silico ADME modeling. Annu Rep Comput Chem 5:101–127

    Article  Google Scholar 

  57. Shao Y, Molnar LF, Jung Y, Kussmann J, Ochsenfeld C, Brown ST, Gilbert AT, Slipchenko LV, Levchenko SV, O’Neill DP, DiStasio RA Jr, Lochan RC, Wang T, Beran GJO, Besley NA, Herbert JM, Lin CY, van Voorhis T, Chien SH, Sodt A, Steele RP, Rassolov VA, Maslen PE, Korambath PP, Adamson RD, Austin B, Baker J, Byrd EFC, Dachsel H, Doerksen RJ, Dreuw A, Dunietz BD, Dutoi AD, Furlani TR, Gwaltney SR, Heyden A, Hirata S, Hsu CP, Kedziora G, Khalliulin RZ, Klunzinger P, Lee AM, Lee MS, Liang W, Lotan I, Nair N, Peters B, Proynov EI, Pieniazek PA, Rhee YM, Ritchie J, Rosta E, Sherrill CD, Simmonett AC, Subotnik JE, Woodcock HL III, Zhang W, Bell AT, Chakraborty AK, Chipman DM, Keil FJ, Warshel A, Hehre WJ, Schaefer HF III, Kong J, Krylov AI, Gill PMW, Head-Gordon M (2006) Advances in methods and algorithms in a modern quantum chemistry program package. Phys Chem Chem Phys 8:3172–3191

    Article  CAS  PubMed  Google Scholar 

  58. Yap CW (2011) PaDEL-Descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474

    Article  CAS  PubMed  Google Scholar 

  59. Livingstone DJ, Salt DW (2005) Variable selection—spoilt for choice? Rev Comput Chem 21:287–348

    Article  CAS  Google Scholar 

  60. Dearden JC, Cronin MTD, Kaiser KLE (2009) How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res 20:241–266

    Article  CAS  PubMed  Google Scholar 

  61. Ballabio D, Consonni V, Mauri A, Claeys-Bruno M, Sergent M, Todeschini R (2014) A novel variable reduction method adapted from space-filling designs. Chemom Intell Lab Syst 136:147–154

    Article  CAS  Google Scholar 

  62. Ambure P, Aher RB, Gajewicz A, Puzyn T, Roy K (2015) “NanoBRIDGES” software: open access tools to perform QSAR and nano-QSAR modeling. Chemom Intell Lab Syst 147:1–13

    Article  CAS  Google Scholar 

  63. Dastmalchi S, Hamzeh-Mivehroud M, Sokouti B (2018) Quantitative structure-activity relationship: a practical approach. CRC Press, Boca Raton

    Book  Google Scholar 

  64. Gramatica P (2013) In: Reisfeld B, Mayeno AN (ed) Computational toxicology, Vol. 2. Springer, New York

  65. Minitab Statistical Software (2017) Minitab 18.1. State College, PA: Minitab, Inc

  66. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148

    Article  Google Scholar 

  67. Martin TM, Harten P, Young DM, Muratov EN, Golbraikh A, Zhu H, Tropsha A (2012) Does rational selection of training and test sets improve the outcome of QSAR modeling? J Chem Inf Model 52:2570–2578

    Article  CAS  PubMed  Google Scholar 

  68. Snarey M, Terrett NK, Willett P, Wilton DJ (1997) Comparison of algorithms for dissimilarity-based compound selection. J Mol Graph Model 15:372–385

    Article  CAS  PubMed  Google Scholar 

  69. BIOVIA, Dassault Systemes (2001) Materials Studio 7.0, San Diego: Dassault Systemes

  70. Liu P, Long W (2009) Current mathematical methods used in QSAR/QSPR studies. Int J Mol Sci 10:1978–1989

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  72. Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MTD, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA, Myatt G, Nikolova-Jeliazkova N, Patlewicz GY, Perkins R, Roberts DW, Schultz TW, Stanton DT, van de Sandt JJM, Tong W, Veith G, Yang C (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern Lab Anim 33(2):155–173

  73. Todeschini R, Consonni V (2000) Handbook of molecular descriptors. WILEY-VCH, Weinheim

    Book  Google Scholar 

  74. Guha R (2008) On the interpretation and interpretability of quantitative structure-activity relationship models. J Comput Aided Mol Des 22:857–871

    Article  CAS  PubMed  Google Scholar 

  75. Ravichandran V, Jain PK, Mourya VK, Agrawal RK (2007) QSAR study on some arylsulfonamides as anti-HIV agents. Med Chem Res 16:342–351

    Article  CAS  Google Scholar 

  76. Papa E, Dearden JC, Gramatica P (2007) Linear QSAR regression for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors. Chemosphere 67:351–358

    Article  CAS  PubMed  Google Scholar 

  77. Wang C, Wei Z, Wang L, Sun P, Wang Z (2015) Assessment of bromine-based ionic liquid toxicity toward aquatic organisms and QSAR analysis. Ecotoxicol Environ Saf 115:112–118

    Article  CAS  PubMed  Google Scholar 

  78. Miyashita Y, Li Z, Sasaki S (1993) Chemical pattern recognition and multivariate analysis for QSAR studies. Trends Anal Chem 12(2):50–60

    Article  CAS  Google Scholar 

  79. Jaiswal M, Khadikar PV, Scozzafava A, Supuran CT (2004) Carbonic anhydrase inhibitors: the first QSAR study on inhibition of tumor-associated isoenzyme IX with aromatic and heterocyclic sulfonamides. Bioorg Med Chem Lett 14:3283–3290

    Article  CAS  PubMed  Google Scholar 

  80. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden JC, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK (2011) Validation of QSAR models—strategies and importance. Int J Drug Des Discov 2(3):511–519

    CAS  Google Scholar 

  82. Muhammad U, Uzairu A, Arthur DE (2018) Review on: quantitative structure activity relationship (QSAR) modeling. J Anal Pharm Res 7(2):240–242

    Article  Google Scholar 

  83. Topliss JG, Costello RJ (1972) Chance correlations in structure-activity studies using multiple regression analysis. J Med Chem 15(10):1066–1068

    Article  CAS  PubMed  Google Scholar 

  84. Kiralj R, Ferreira MMC (2009) Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J Braz Chem Soc 20(4):770–787

    Article  CAS  Google Scholar 

  85. Sheridan RP (2012) Three useful dimensions for domain applicability in QSAR models using random forest. J Chem Inf Model 52:814–823

    Article  CAS  PubMed  Google Scholar 

  86. Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Consonni V, Ballabio D, Todeschini R (2010) Evaluation of model predictive ability by external validation techniques. J Chemom 24:194–201

    Article  CAS  Google Scholar 

  88. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26(5):694–701

    Article  CAS  Google Scholar 

  89. Roy K, Mitra I (2011) On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Comb Chem High Throughput Screen 14:450–474

    Article  CAS  PubMed  Google Scholar 

  90. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476–488

    Article  CAS  PubMed  Google Scholar 

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  1. Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria

    Lukman Kehinde Akinola, Adamu Uzairu, Gideon Adamu Shallangwa & Stephen Eyije Abechi

  2. Department of Chemistry, Bauchi State University, Gadau, Nigeria

    Lukman Kehinde Akinola

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  1. Lukman Kehinde Akinola
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  2. Adamu Uzairu
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All authors contributed to the study conception and design. Data collection and analysis were performed by Lukman K. Akinola. The first draft of the manuscript was written by Lukman K. Akinola and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Akinola, L.K., Uzairu, A., Shallangwa, G.A. et al. Quantitative structure–activity relationship modeling of hydroxylated polychlorinated biphenyls as constitutive androstane receptor agonists. Struct Chem 34, 477–490 (2023). https://doi.org/10.1007/s11224-022-01992-2

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