Application of Biotechnology in Cancer research
Cancer research and drug development have advanced significantly with the rise of new technologies like whole-genome sequencing, proteome analysis, and exome sequencing. These methods have provided fresh insights and information. In the early stages, drug discovery typically begins in academic settings, where researchers explore and lay the foundation for targeting specific proteins, pathways, or cellular networks to achieve therapeutic effects.
Over the last decade, there has been significant progress in understanding cancer and developing targeted treatments. Molecular research has identified key mutations in various cancer types, allowing for the creation of drugs aimed at these specific targets. Another breakthrough was understanding that cells within the same tumor can vary in their characteristics, which explains why some treatments work differently for primary tumors compared to metastases in the same patient. Additionally, researchers have uncovered how tumor cells interact with their surrounding environment, which plays a critical role in the spread of cancer. Finally, important insights have been gained into how tumors develop resistance to therapies, helping to improve drug development.
Recent advancements in biotechnology emphasize the importance of creating personalized cancer treatments. For each patient, using tools like gene sequencing, protein analysis, and studying intracellular signals or miRNA profiles is shaping the concept of personalized medicine and patient-specific therapies. The most promising technologies in this area include next-generation sequencing (NGS), gene and protein array technologies, and identifying microRNAs that can be used as anticancer drugs.
Identifying and validating drug targets involves key challenges because the targets need to meet several important criteria:
- They must be safe.
- They should lead to effective treatment when targeted.
- They must address a previously unmet medical need.
- They should be easy for the drug to reach and interact with.
Once these criteria are met, the interaction between the drug and the target should result in a measurable biological response, both in lab settings (in vitro) and in living organisms (in vivo).
Next-generation sequencing – innovative complex instrument in drug discovery
The ENCODE project, launched in 2003, led to advancements in high-throughput sequencing and computational techniques to study how genes are expressed and regulated in complex molecular and signaling networks. Given the diverse genetic makeup of tumors, Next-Generation Sequencing (NGS) technologies have enabled the development of personalized cancer treatments, allowing clinicians to choose the most effective drugs for individual patients. Highlighting the role of NGS in cancer drug discovery is key to identifying new anticancer therapies.
MicroRNA-based technology
MicroRNAs (miRNAs) are small molecules that play a significant role in controlling cellular activities by regulating protein production after the proteins are initially made. In recent years, these miRNAs have gained attention both as disease markers and potential drugs. When it comes to diseases like cancer, changes in miRNA levels have been linked to the development and progression of tumors. Depending on the situation, they can either promote or suppress cancer growth. This makes specific miRNAs, or even groups of them, useful for diagnosing cancers or monitoring treatment effectiveness. Moreover, certain miRNAs circulating in the blood have been found to correspond with how well patients respond to chemotherapy. This means they could serve as markers for predicting the success of personalized cancer treatments. Recent discoveries in cancer research show that miRNAs are promising targets for developing new cancer therapies.
Technology optimization
The key step in drug discovery is finding new molecules (or repurposing existing ones) that can be used as anticancer drugs. A crucial part of this process involves using high-throughput screening (HTS) techniques. One important stage focuses on improving screening technology and heavily relying on robotics. During this optimization process, bioinformatics and computational methods play a significant role in identifying potential new drug candidates from various compound classes.
HTS can be a cost-effective approach for discovering new drug molecules. To enhance its effectiveness, it can be combined with computational methods like virtual screening, which integrate structural data with traditional lead optimization strategies.
Conclusion
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