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Resources for Scientific and Technical Information for Designing Regulatory Submissions
Corresponding Author(s) : M. Mahesh
International Journal of Allied Medical Sciences and Clinical Research,
Vol. 12 No. 4 (2024): 2024 Volume -12 - Issue 4
Abstract
The pace of scientific progress over the past several decades within the biological, drug development, and the digital realm has been remarkable. The'omics revolution has enabled a better understanding of the biological basis of disease, unlocking the possibility of new products such as gene and cell therapies which offer novel patient centric solutions. Innovative approaches to clinical trial designs promise greater efficiency, and in recent years, scientific collaborations, and consortia have been developing novel approaches to leverage new sources of evidence such as real-world data, patient experience data, and biomarker data. Alongside this there have been great strides in digital innovation. Cloud computing has become mainstream and the internet of things and block chain technology have become a reality. These examples of transformation stand in sharp contrast to the current inefficient approach for regulatory submission, review, and approval of medicinal products. This process has not fundamentally changed since the beginning of medicine regulation in the late 1960s. Fortunately, progressive initiatives are emerging that will enrich and streamline regulatory decision making and deliver patient centric therapies, if they are successful in transforming the current transactional construct and harnessing scientific and technological advances. Such a radical transformation will not be simple for both regulatory authorities and company sponsors, nor will progress be linear. We examine the shortcomings of the current system with its entrenched and variable business processes, offer examples of progress as catalysts for change, and make the case for a new cloud based model. To optimize navigation toward this reality we identify implications and regulatory design questions which must be addressed. We conclude that a new model is possible and is slowly emerging through cumulative change initiatives that question, challenge, and redesign best practices, roles, and responsibilities, and that this must be combined with adaptation of behaviors and acquisition of new skills.
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- Lin Y, Zhang Q, Zhang HM, Liu W, Liu CJ, Li Q, et al. Transcription factor and miRNA co-regulatory network reveals shared and specific regulators in the development of B cell and T cell. Sci Rep 2015;5:15215.
- Zhang Q, Hu H, Chen SY, Liu CJ, Hu FF, Yu J, et al. Transcriptome and regulatory network analyses of CD19-CAR-T immunotherapy for B-ALL. Genomics Proteomics Bioinformatics 2019;17:190–200.
- Kadonaga JT. Regulation of RNA polymerase II transcription by sequence-specific DNA binding factors. Cell 2004;116:247–57.
- Lee TI, Young RA. Transcriptional regulation and its misregulation in disease. Cell 2013;152:1237–51.
- Tang Q, Zhang Q, Lv Y, Miao YR, Guo AY. SEGreg: a database for human specifically expressed genes and their regulations in cancer and normal tissue. Brief Bioinform 2019;20:1322–8.
- Zhang Q, Liu W, Liu C, Lin SY, Guo AY. SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data. Brief Bioinform 2018;19:1325–36.
- Kaufmann K, Muin˜o JM, Østera˚s M, Farinelli L, Krajewski P, Angenent GC. Chromatin immunoprecipitation (ChIP) of plant transcription factors followed by sequencing (ChIP-SEQ) or hybridization to whole genome arrays (ChIP-CHIP). Nat Protoc 2010;5:457–72.
- ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 2012;489:57–74.
- Che`neby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments. Nucleic Acids Res 2018;46:D267–75.
- Mei S, Qin Q, Wu Q, Sun H, Zheng R, Zang C, et al. Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse. Nucleic Acids Res 2017;45:D658–62.
- Wang J, Zhuang J, Iyer S, Lin XY, Greven MC, Kim BH, et al. Factorbook.org: a Wiki-based database for transcription factorbinding data generated by the ENCODE consortium. Nucleic Acids Res 2013;41:D171–6.
- Zhou KR, Liu S, Sun WJ, Zheng LL, Zhou H, Yang JH, et al. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. Nucleic Acids Res 2017;45:D43–50.
- Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 2018;46:D380–6.
- Thompson PJ, Macfarlan TS, Lorincz MC. Long terminal repeats: from parasitic elements to building blocks of the transcriptional regulatory repertoire. Mol Cell 2016;62:766–76.
- Zhou X, Maricque B, Xie M, Li D, Sundaram V, Martin EA, et al. The Human Epigenome Browser at Washington University. Nat Methods 2011;8:989–90.
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol 2008;9:R137.
- Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res 2012;22:1798–812.
- Ma W, Noble WS, Bailey TL. Motif-based analysis of large nucleotide data sets using MEME-ChIP. Nat Protoc 2014;9:1428–50.
- Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics 2011;27:1017–8.
- Tang Q, Chen Y, Meyer C, Geistlinger T, Lupien M, Wang Q, et al. A comprehensive view of nuclear receptor cancer cistromes. Cancer Res 2011;71:6940–7.
References
Lin Y, Zhang Q, Zhang HM, Liu W, Liu CJ, Li Q, et al. Transcription factor and miRNA co-regulatory network reveals shared and specific regulators in the development of B cell and T cell. Sci Rep 2015;5:15215.
Zhang Q, Hu H, Chen SY, Liu CJ, Hu FF, Yu J, et al. Transcriptome and regulatory network analyses of CD19-CAR-T immunotherapy for B-ALL. Genomics Proteomics Bioinformatics 2019;17:190–200.
Kadonaga JT. Regulation of RNA polymerase II transcription by sequence-specific DNA binding factors. Cell 2004;116:247–57.
Lee TI, Young RA. Transcriptional regulation and its misregulation in disease. Cell 2013;152:1237–51.
Tang Q, Zhang Q, Lv Y, Miao YR, Guo AY. SEGreg: a database for human specifically expressed genes and their regulations in cancer and normal tissue. Brief Bioinform 2019;20:1322–8.
Zhang Q, Liu W, Liu C, Lin SY, Guo AY. SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data. Brief Bioinform 2018;19:1325–36.
Kaufmann K, Muin˜o JM, Østera˚s M, Farinelli L, Krajewski P, Angenent GC. Chromatin immunoprecipitation (ChIP) of plant transcription factors followed by sequencing (ChIP-SEQ) or hybridization to whole genome arrays (ChIP-CHIP). Nat Protoc 2010;5:457–72.
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 2012;489:57–74.
Che`neby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments. Nucleic Acids Res 2018;46:D267–75.
Mei S, Qin Q, Wu Q, Sun H, Zheng R, Zang C, et al. Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse. Nucleic Acids Res 2017;45:D658–62.
Wang J, Zhuang J, Iyer S, Lin XY, Greven MC, Kim BH, et al. Factorbook.org: a Wiki-based database for transcription factorbinding data generated by the ENCODE consortium. Nucleic Acids Res 2013;41:D171–6.
Zhou KR, Liu S, Sun WJ, Zheng LL, Zhou H, Yang JH, et al. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. Nucleic Acids Res 2017;45:D43–50.
Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 2018;46:D380–6.
Thompson PJ, Macfarlan TS, Lorincz MC. Long terminal repeats: from parasitic elements to building blocks of the transcriptional regulatory repertoire. Mol Cell 2016;62:766–76.
Zhou X, Maricque B, Xie M, Li D, Sundaram V, Martin EA, et al. The Human Epigenome Browser at Washington University. Nat Methods 2011;8:989–90.
Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol 2008;9:R137.
Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res 2012;22:1798–812.
Ma W, Noble WS, Bailey TL. Motif-based analysis of large nucleotide data sets using MEME-ChIP. Nat Protoc 2014;9:1428–50.
Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics 2011;27:1017–8.
Tang Q, Chen Y, Meyer C, Geistlinger T, Lupien M, Wang Q, et al. A comprehensive view of nuclear receptor cancer cistromes. Cancer Res 2011;71:6940–7.