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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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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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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Artificial intelligence, machine learning, and drug repurposing in cancer.

Ziaurrehman Tanoli1, Markus Vähä-Koskela1, Tero Aittokallio1,2,3

  • 1Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland.

Expert Opinion on Drug Discovery
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Summary

Drug repurposing uses machine learning (ML) and artificial intelligence (AI) to find new uses for existing drugs. This computational approach accelerates drug discovery for various diseases, including cancer and COVID-19.

Keywords:
Drug repurposingartificial intelligencemachine learningprecision oncologytarget repositioning

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Area of Science:

  • Computational drug discovery
  • Pharmacology
  • Biotechnology

Background:

  • Drug repurposing offers a cost-effective strategy for identifying new therapeutic applications for existing medications.
  • Machine learning (ML) and artificial intelligence (AI) are increasingly utilized for systematic drug repurposing, leveraging big data resources to accelerate and de-risk the drug development pipeline.

Purpose of the Study:

  • To review supervised ML and AI methods for drug repurposing using publicly available data.
  • To highlight the application of comprehensive target activity profiles for systematic drug repurposing.
  • To discuss the applicability of these methods to various indications, including anticancer therapies and COVID-19 treatments.

Main Methods:

  • Focus on supervised machine learning and artificial intelligence techniques.
  • Utilizes publicly available databases and information resources.
  • Employs comprehensive target activity profiles, including off-target information, to identify repurposing candidates.

Main Results:

  • The reviewed methods are applicable to a wide range of indications, with a focus on anticancer drug therapies.
  • Extending drug target profiles to include therapeutically relevant off-targets enhances the systematic repurposing process.
  • Functional testing of patient cells provides valuable data for tissue-aware AI approaches in drug repurposing.

Conclusions:

  • Supervised ML and AI methods, particularly those utilizing comprehensive target activity profiles, offer a powerful approach to drug repurposing.
  • The integration of functional testing data can overcome limitations of genomics-only approaches in cancer drug repurposing.
  • These computational strategies hold significant promise for accelerating drug discovery and development across diverse medical indications.