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Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
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Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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Predicting Drug Resistance Using Deep Mutational Scanning.

Gur Pines1, Reilly G Fankhauser2, Carrie A Eckert3,4

  • 1Department of Entomology, Agricultural Research Organization, Volcani Center, P.O.B 15159, Rishon LeZion 7505101, Israel.

Molecules (Basel, Switzerland)
|May 15, 2020
PubMed
Summary
This summary is machine-generated.

Developing new drug inhibitors is crucial due to widespread drug resistance. Genome editing technologies can predict resistance mutations by testing large libraries, aiding drug development, particularly for malaria.

Keywords:
DXRdrug resistancefosmidomycingenome editingsequence to activity mapping

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Area of Science:

  • Genomics
  • Drug Discovery
  • Molecular Biology

Background:

  • Drug resistance poses a significant global health threat, necessitating novel therapeutic strategies.
  • Understanding resistance mechanisms is key to developing effective new drug inhibitors.
  • Malaria treatment is hampered by increasing parasite resistance to existing drugs.

Purpose of the Study:

  • To present a novel approach for predicting drug resistance mutations using genome editing.
  • To highlight the application of high-throughput DNA synthesis and sequencing in mutagenesis library screening.
  • To discuss the relevance of this methodology for drug development and combating malaria resistance.

Main Methods:

  • Utilizing genome editing technologies for large-scale mutagenesis library creation.
  • Employing high-throughput DNA sequencing to identify resistance-conferring mutations.
  • Analyzing mutation data to understand drug resistance mechanisms.

Main Results:

  • Demonstrated the feasibility of predicting drug resistance mutations through large-scale mutagenesis screening.
  • Provided examples illustrating the approach's utility in identifying resistance mechanisms.
  • Established the relevance of this method for accelerating drug development pipelines.

Conclusions:

  • Genome editing combined with high-throughput sequencing offers a powerful strategy to predict and overcome drug resistance.
  • This approach can significantly expedite the development of new therapeutic agents, especially against challenging diseases like malaria.
  • Further application of this methodology holds promise for addressing the evolving challenge of antimicrobial and antiparasitic drug resistance.