Biomarkers and Bioactivity in Drug Discovery using a Joint Modelling Approach
DOI:
https://doi.org/10.18034/mjmbr.v8i2.585Keywords:
Biomarker, Bioactivity, Bio-pharma, Drug Discovery, Joint Modelling ApproachAbstract
Biomarkers that are validated and robust are required for the enhancement of diagnosis, the observation of drug-related activity, therapeutic reactions, and as the blueprint for developing safer and more direct therapeutic efforts for a variety of chronic ailments. Various kinds of biomarkers have proven impactful when it comes to the discovery and development of drugs, but the procedure that involves identifying and verifying ailment-specific biomarkers has proven to be hassling. In recent times, there have been some advancements in multiple omics (also known as multi-omics) methods like transcriptomic, cytometry, genomics, proteomics, metabolomics and imaging. These advancements have made it possible for the discovery and development of distinct biomarkers for complicated chronic ailments to be accelerated expeditiously. In spite of the fact that numerous drawbacks still need to be looked into, ongoing efforts for the discovery and improvement of illness-associated biomarkers will go a long way in optimizing decision-making across the entire process of drug development and expand our comprehension of the infection processes. In addition, when the preclinical biomarkers are effectively translated into the clinic, the way will pave well to an equally effective implementation of personalized therapies throughout complicated illness environments to become beneficial to patients, healthcare service providers and the industry of bio-pharma.
Downloads
References
Abedin, M. M. M., Ahmed, A. A. A., and Neogy, T. K. (2012). Mechanism of Accountability and Auditing: Public Sector Scenarios of Bangladesh. Journal of Business Studies, 4, 131-148.
Ahmed, A. A. A. & Dey, M. M. (2009). Timeliness attributes and the extent of accounting disclosure: a study of banking companies in Bangladesh. Osmania Journal of International Business Studies, 4(1).
Ahmed, A. A. A. (2009). The Effect of Timeliness Regulation of Corporate Financial Reporting: Evidence from Banking Sector of Bangladesh. Accounting and Management Information Systems, 8(2), 216 - 235. http://online-cig.ase.ro/jcig/art/8_2_4.pdf
Ahmed, A. A. A., & Dey, M. M. (2009). Corporate Attribute and the Extent of Disclosure: A Study of Banking Companies in Bangladesh. Proceedings of the 5th International Management Accounting Conference (IMAC), OCT 19-21, 2009, UKM, Kuala Lumpur, MALAYSIA, Pages: 531-553. https://publons.com/publon/11427801/
Ahmed, A. A. A., Hussain, S., Kurniullah, A. Z., Ramirez-Asis, E., Al-Awawdeh, N., Al-Shamayleh, N. J. M., Julca-Guerrero, F. (2021). Protection against Letters of Credit Fraud. Journal of Legal, Ethical and Regulatory Issues, 24(Special Issue 1), 1-11. https://doi.org/10.5281/zenodo.5507840
Ahmed, A. A. A., Khan, W., & Hossain, M. S. (2011). Reporting Practice of Accounting Disclosure on Changes in Listed Companies of Bangladesh. ASA University Review, 5(1), 83-96. https://www.researchgate.net/publication/336664901
Amin, R., & Manavalan, M. (2017). Modeling Long Short-Term Memory in Quantum Optical Experiments. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 6–13. Retrieved from https://upright.pub/index.php/ijrsps/article/view/48
Azad, M. R., Khan, W., & Ahmed, A. A. A. (2011). HR Practices in Banking Sector on Perceived Employee Performance: A Case of Bangladesh. Eastern University Journal, 3(3), 30–39. https://doi.org/10.5281/zenodo.4043334
Bai, J. P. F., A. V. Alekseyenko, A. Statnikov, I.-M. Wang and P. H. Wong (2013). Strategic applications of gene expression: from drug discovery/development to bedside. AAPS J., 15, 427–437. DOI: https://doi.org/10.1208/s12248-012-9447-1
Begum, R., Ahmed, A. A. A., & Neogy. T. K. (2012). Management Decisions and Univariate Analysis: Effects on Corporate Governance in Bangladesh. Journal of Business Studies, 3, 87-115.
Benjamini, Y. and Y. Hochberg (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300. DOI: https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Buyse, M. and G. Molenberghs (1998). The validation of surrogate endpoints in randomized experiments. Biometrics, 54, 186–201. DOI: https://doi.org/10.2307/2533853
Bynagari, N. B. (2018). On the ChEMBL Platform, a Large-scale Evaluation of Machine Learning Algorithms for Drug Target Prediction. Asian Journal of Applied Science and Engineering, 7, 53–64. Retrieved from https://upright.pub/index.php/ajase/article/view/31
Bynagari, N. B. (2020). The Difficulty of Learning Long-Term Dependencies with Gradient Flow in Recurrent Nets. Engineering International, 8(2), 127-138. https://doi.org/10.18034/ei.v8i2.570
Bynagari, N. B., & Amin, R. (2019). Information Acquisition Driven by Reinforcement in Non-Deterministic Environments. American Journal of Trade and Policy, 6(3), 107-112. https://doi.org/10.18034/ajtp.v6i3.569
Dearden, J. C. (2003). In silico prediction of drug toxicity. J. Comput. Aided Mol. Des., 17, 119–127. DOI: https://doi.org/10.1023/A:1025361621494
Doewes, R. I.; Ahmed, A. A. A.; Bhagat, A.; Nair, R.; Donepudi, P. K.; Goon, S.; Jain, V.; Gupta, S.; Rathore, N. K.; Jain, N. K. (2021). A regression analysis based system for sentiment analysis and a method thereof. Australian Official Journal of Patents, 35(17), Patent number: 2021101792. https://lnkd.in/gwsbbXa
Donepudi, P. K. (2014a). Technology Growth in Shipping Industry: An Overview. American Journal of Trade and Policy, 1(3), 137-142. https://doi.org/10.18034/ajtp.v1i3.503 DOI: https://doi.org/10.18034/ajtp.v1i3.503
Donepudi, P. K. (2014b). Voice Search Technology: An Overview. Engineering International, 2(2), 91-102. https://doi.org/10.18034/ei.v2i2.502 DOI: https://doi.org/10.18034/ei.v2i2.502
Donepudi, P. K. (2015). Crossing Point of Artificial Intelligence in Cybersecurity. American Journal of Trade and Policy, 2(3), 121-128. https://doi.org/10.18034/ajtp.v2i3.493 DOI: https://doi.org/10.18034/ajtp.v2i3.493
Donepudi, P. K. (2016). Influence of Cloud Computing in Business: Are They Robust?. Asian Journal of Applied Science and Engineering, 5(3), 193-196. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1181
Donepudi, P. K., & Bynagari, N. B. (2021). Prediction of Transfusion Based on Machine Learning. Int. J. of Aquatic Science, 12(3), 2168-2180. http://www.journal-aquaticscience.com/article_136518.html
Eriksson, S., S. E. Prast-Nielsen, E. Flaberg, L. Szekely and E. Arner (2009). High levels of thioredoxin reductase 1 modulate drug-specific cytotoxic efficacy. Free Radic. Biol. Med., 47, 1661–1671. DOI: https://doi.org/10.1016/j.freeradbiomed.2009.09.016
Fadiel, A. and F. Naftolin (2003). Microarray applications and challenges: a vast array of possibilities. Reprod. Sci., 1, 1111–1121.
Gorrini, C., I. S. Harris and T. W. Mak (2013). Modulation of oxidative stress as an anticancer strategy. Nat. Rev. Drug Discov., 12, 931–947. DOI: https://doi.org/10.1038/nrd4002
Hasumi, H., M. Baba, S. Hong, Y. Hasumi, Y. Huang, M. Yao, V. Valera, W. Linehan and L. Schmidt (2008). Identification and characterization of a novel folliculin-interacting protein fnip2. Gene, 415, 60–67. DOI: https://doi.org/10.1016/j.gene.2008.02.022
Lin, D., Z. Shkedy, G. Molenberghs, W. Talloen, H. Gohlmann and L. Bijnens (2010). Selection and evaluation of gene-specific biomarkers in pre-clinical and clinical microarray experiments. Online J. Bioinform., 11, 106–127.
Manavalan, M. (2018). Do Internals of Neural Networks Make Sense in the Context of Hydrology?. Asian Journal of Applied Science and Engineering, 7, 75–84. Retrieved from https://upright.pub/index.php/ajase/article/view/41
Manavalan, M. (2019). P-SVM Gene Selection for Automated Microarray Categorization. International Journal of Reciprocal Symmetry and Physical Sciences, 6, 1–7. Retrieved from https://upright.pub/index.php/ijrsps/article/view/43
Manavalan, M. (2019). Using Fuzzy Equivalence Relations to Model Position Specificity in Sequence Kernels. Asian Journal of Applied Science and Engineering, 8, 51–64. Retrieved from https://upright.pub/index.php/ajase/article/view/42
Manavalan, M. (2020). Intersection of Artificial Intelligence, Machine Learning, and Internet of Things – An Economic Overview. Global Disclosure of Economics and Business, 9(2), 119-128. https://doi.org/10.18034/gdeb.v9i2.584 DOI: https://doi.org/10.18034/gdeb.v9i2.584
Manavalan, M., & Donepudi, P. K. (2016). A Sample-based Criterion for Unsupervised Learning of Complex Models beyond Maximum Likelihood and Density Estimation. ABC Journal of Advanced Research, 5(2), 123-130. https://doi.org/10.18034/abcjar.v5i2.581
Manojkumar, P., Suresh, M., Ahmed, A. A. A., Panchal, H., Rajan, C. C. A., Dheepanchakkravarthy, A., Geetha, A., Gunapriya, B., Mann, S., & Sadasivuni, K. K. (2021). A novel home automation distributed server management system using Internet of Things. International Journal of Ambient Energy, https://doi.org/10.1080/01430750.2021.1953590 DOI: https://doi.org/10.1080/01430750.2021.1953590
Martin, Y. C., J. L. Kofron and L. M. Traphagen (2002). Do structurally similar molecules have similar biological activity? J. Med. Chem., 45, 4350–4358. DOI: https://doi.org/10.1021/jm020155c
Nantasenamat, C., C. Isarankura-Na-Ayudhya, T. Naenna and V. Prachayasittikul (2009). A practical overview of quantitative structure-activity relationship. EXCLI J., 8, 74–78.
Neogy, T. K. and Ahmed, A. A. A. (2015). The Extent of Disclosure of Different Components of Disclosure Index: A Study on Commercial Banks in Bangladesh. Global Disclosure of Economics and Business, 4(2), 100-110. https://doi.org/10.18034/gdeb.v4i2.139 DOI: https://doi.org/10.18034/gdeb.v4i2.139
Panchal, H., Sadasivuni, K. K., Ahmed, A. A. A., Hishan, S. S., Doranehgard, M. H., Essa, F. A., Shanmugan, S., & Khalid, M. (2021). Graphite powder mixed with black paint on the absorber plate of the solar still to enhance yield: An experimental investigation. Desalination, Volume 520. https://doi.org/10.1016/j.desal.2021.115349 DOI: https://doi.org/10.1016/j.desal.2021.115349
Prasanth Kumar, S., Y. T. Jasrai, H. A. Pandya and R. M. Rawal (2015). Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions. J. Biomol. Struct. Dyn., 33, 56–69. DOI: https://doi.org/10.1080/07391102.2013.849618
Raya, I., Kzar, H. H., Mahmoud, Z. H., Ahmed, A. A. A., Ibatova, A. Z., & Kianfar, E. (2021). A review of gas sensors based on carbon nanomaterial. Carbon Letters. Article No: 276. https://doi.org/10.1007/s42823-021-00276-9 DOI: https://doi.org/10.1007/s42823-021-00276-9
Rogers, D. and Hahn, M. (2010). Extended-connectivity fingerprints. J. Chem. Inf. Model., 50, 742–754. DOI: https://doi.org/10.1021/ci100050t
Shaib, W., R. Mahajan and B. El-Rayes (2013). Markers of resistance to anti-egfr therapy in colorectal cancer. J. Gastrointest. Oncol., 4, 303–318.
Sharma, D. K., Chakravarthi, D. S., Shaikh, A. A., Ahmed, A. A. A., Jaiswal, S., Naved, M. (2021). The aspect of vast data management problem in healthcare sector and implementation of cloud computing technique. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.07.388 DOI: https://doi.org/10.1016/j.matpr.2021.07.388
Siddique, M. N. & Ahmed, A. A. A. (2015). Congruence of Competitive Advantage and Transfer Pricing: A Study on Selected MNCs Operating in Bangladesh. Asian Accounting & Auditing Advancement, 5(2), 119-126. https://www.researchgate.net/publication/354712086
Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol., 3, 397–420. DOI: https://doi.org/10.2202/1544-6115.1027
Zhu, Y., Kamal, E. M., Gao, G., Ahmed, A. A. A., Asadullah, A., Donepudi, P. K. (2021). Excellence of Financial Reporting Information and Investment Productivity. International Journal of Nonlinear Analysis and Applications, 12(1), 75-86. https://doi.org/10.22075/ijnaa.2021.4659
--0--