Genomic Profiling for Predictive Treatment Strategies in Fibrotic Interstitial Lung Disease
Abstract
:1. Introduction
2. Moving Pharmacogenomics from Bench to Bedside
3. Idiopathic Pulmonary Fibrosis
3.1. Efficacy of Antifibrotic Agents
3.2. Safety of Antifibrotic Agents
4. Connective Tissue Disease Related Interstitial Lung Disease
4.1. Efficacy
4.2. Safety
5. Other Fibrotic Interstitial Lung Diseases
6. Current Ongoing Trials and Future Perspective
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Pharmacogenetics (PGt) | Pharmacogenomics (PGx) | |
---|---|---|
Definition | It is the study of how variations in an individual’s DNA sequence affect their response to drugs. | It is the broader study of how an individual’s entire genome, including DNA and RNA, influences their response to drugs. |
Focus | PGt primarily examines variations in specific genes known to impact drug metabolism, transport, and receptor interactions. | PGx investigates how variations across the entire genome affect drug response, encompassing not only coding regions but also non-coding regions that regulate gene expression. |
Methodology | It investigates single genetic variants, such as single nucleotide polymorphisms (SNPs), in key drug-metabolizing enzymes, transporters, or receptors. | It employs genome-wide approaches to analyse genetic variations, including SNPs, insertions/deletions, copy number variations, and structural variants. |
Applications | PGt is used to predict how individuals may respond to specific drugs based on their genetic makeup, helping to tailor medication dosages and select the most appropriate treatments for patients. | PGx provides a more comprehensive understanding of the genetic basis of drug response, facilitating the discovery of novel biomarkers and therapeutic targets. It can also inform personalized medicine approaches by considering a broader range of genetic factors influencing drug efficacy and safety. |
CTD | Gene | Chromosome | SNP Description Genetic Alteration | Clinical Consequences | Refs. |
---|---|---|---|---|---|
RA | P-gp | 7q21.12 | 3435C > T 2677G > T 1236C > T | ↓ CSs transport activity ↓ Intracellular CSs concentrations | [66,67] |
P-gp | 7q21.12 | 3435C > T | ↓ MTX response | [68] | |
IRGs | / | ↑ Expression | ↓ RTX response | [69] | |
FCGR3A | 1q23.3 | 158V > F; FF 158 (FF) | ↑ MTX response ↑ Infliximab, adalimumab and etanercept response | [70,71] | |
BAFF BAFF (promoter) | 13q33.3 13 | 871 (CC) TTTT | ↑ RTX response ↑ RTX response in seropositive pts | [70,72] | |
IL-6 (promoter) | 7 | 174 (CC) | ↓ RTX response compared to GC/GG | [73] | |
IFI6 | 1p35.3 | ↑ Expression | ↑ TCZ response | [74] | |
MX2 | 21q22.3 | ↑ Expression | ↑ TCZ response | [74] | |
OASL | 12q24.31 | ↑ Expression | ↑ TCZ response | [74] | |
IMPDH | 7q32.1 | rs11706052 | ↓ MPA efficacy to inhibit lymphocyte proliferation (in vitro) | [75] | |
TMPT | 6q22.3 | TPMT*3A | ↑ AZA-related hematopoietic and gastrointestinal toxicity | [76] | |
SS | FCGR3A | 1q23.3 | 158V > F 158 (FF) | No effect on RTX response | [77] |
SSc | TMPT | 6q22.3 | rs1801133 | ↑ Risk for high systolic pressure in pts under AZA | [76] |
HLA-DRB1 | 6q21.32 | * 1304 * 0407 | ↑ Risk SRC | [78] | |
GPATCH2L | 14q24.3 | rs935332 | ↑ Risk SRC in ARA-positive pts | [79] | |
Sarcoidosis | TNF-α | 308 (GA/AA) 308 (GG) | ↑ Probability of spontaneous resolution ↑ Risk of negative prognosis | [80,81,82] |
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Perrotta, F.; Sanduzzi Zamparelli, S.; D’Agnano, V.; Montella, A.; Fomez, R.; Pagliaro, R.; Schiattarella, A.; Cazzola, M.; Bianco, A.; Mariniello, D.F. Genomic Profiling for Predictive Treatment Strategies in Fibrotic Interstitial Lung Disease. Biomedicines 2024, 12, 1384. https://doi.org/10.3390/biomedicines12071384
Perrotta F, Sanduzzi Zamparelli S, D’Agnano V, Montella A, Fomez R, Pagliaro R, Schiattarella A, Cazzola M, Bianco A, Mariniello DF. Genomic Profiling for Predictive Treatment Strategies in Fibrotic Interstitial Lung Disease. Biomedicines. 2024; 12(7):1384. https://doi.org/10.3390/biomedicines12071384
Chicago/Turabian StylePerrotta, Fabio, Stefano Sanduzzi Zamparelli, Vito D’Agnano, Antonia Montella, Ramona Fomez, Raffaella Pagliaro, Angela Schiattarella, Mario Cazzola, Andrea Bianco, and Domenica Francesca Mariniello. 2024. "Genomic Profiling for Predictive Treatment Strategies in Fibrotic Interstitial Lung Disease" Biomedicines 12, no. 7: 1384. https://doi.org/10.3390/biomedicines12071384
APA StylePerrotta, F., Sanduzzi Zamparelli, S., D’Agnano, V., Montella, A., Fomez, R., Pagliaro, R., Schiattarella, A., Cazzola, M., Bianco, A., & Mariniello, D. F. (2024). Genomic Profiling for Predictive Treatment Strategies in Fibrotic Interstitial Lung Disease. Biomedicines, 12(7), 1384. https://doi.org/10.3390/biomedicines12071384