Finding New Ways to Drug "Difficult to Drug" Targets

Development of Novel Warheads for Covalent Inhibitors.

 Covalent inhibitors have a rich history spanning over a century, with over 50 FDA-approved drugs designed to combat cancer, infectious diseases, as well as central nervous system and cardiovascular disorders. Small molecule drugs that irreversibly bind covalently to their target proteins present distinct advantages over traditional reversible inhibitors. These benefits encompass prolonged action duration, less frequent drug dosing, reduced pharmacokinetic sensitivity, and the potential to target proteins considered previously "undruggable."

Historically, concerns about toxicity resulting from non-specific modifications of proteins, nucleic acids, and other crucial biomolecules, along with conjugation with glutathione, have hindered the progress of covalent drugs. However, contemporary interest in developing covalent inhibitors has surged due to advancements in synthetic organic and medicinal chemistry tools. These advancements have led to the creation of novel covalent warheads, coupled with a profound understanding of their chemical reactivities within living organisms. This has (re)opened doors to opportunities that were once deemed unattainable with classical small molecule inhibitors.

The mechanism of action for covalent inhibitors involves a two-step process: equilibrium bond formation followed by covalent bond formation. The resulting drug-protein complex is irreversible, distinguishing it from complexes formed with conventional equilibrium bonds. This complex arises from the reaction between the nucleophile of the protein (thiols for cysteine, hydroxy group for serine, threonine, and tyrosine, and amino group for lysine) and the electrophilic warhead.

Our ongoing work involves the development of new acrylamide-type warheads, leveraging strain-release transformations. These novel warheads exhibit high versatility due to the tunability of their electronic properties, steric hindrance, and the configuration of one or multiple stereocenters in proximity to the reactive site. This innovative design of covalent inhibitors places the warhead in a pivotal role in determining substrate specificity, presenting exciting opportunities for drug discovery and development campaigns.

Protein-Protein Interaction Inhibitors: Development of Selective Inhibitors of the Master Regulator of Anti-oxidative Responses Nrf2 

Our research endeavors are centered on the development of selective inhibitors targeting Nrf2, the master regulator of antioxidative responses. The rationale behind focusing on Nrf2 stems from the stress conditions tumors face, such as nutrient deprivation, hypoxia, and acidic environments. To survive and proliferate, cancer cells activate defense mechanisms, including the overactivation of Nrf2, countering the effects of various anticancer agents and promoting drug resistance. 

Nrf2, a cytoplasmic transcription factor, undergoes proteasomal degradation under physiological conditions, regulated by KEAP1-mediated ubiquitination, resulting in a short half-life. However, during oxidative insults, Nrf2 translocates to the nucleus, where it interacts with ARE sequences, expressing thousands of antioxidative genes. While Nrf2's promotion of antioxidative defense is vital for cell survival and organismal homeostasis, its over-activation can contribute to the onset and/or exacerbation of diseases.

The challenge in targeting Nrf2 lies in the transient nature of transcription factors, posing difficulties in developing promising inhibitors. 

The potential outcomes of developing selective Nrf2 inhibitors are substantial. These inhibitors would shed light on downstream biological pathways governed by this crucial transcription factor, providing new insights into the pathogenesis of diseases such as Alzheimer's and Parkinson's. Moreover, by developing inhibitors targeting Nrf2, we aim to disrupt this defense mechanism, making cancer cells more susceptible to the effects of anticancer drugs. Inhibiting Nrf2 activity can enhance the effectiveness of existing anticancer therapies and potentially overcome drug resistance.

Development of Artificial Intelligence for Drug Discovery

 Artificial Intelligence (AI) plays a pivotal role in revolutionizing drug discovery and development within the pharmaceutical industry. In the initial phases, AI algorithms analyze biological data to identify potential drug targets by understanding the molecular mechanisms underlying diseases. Machine learning models then validate the relevance of these targets by predicting their association with specific diseases. Once potential targets are identified, AI is employed in the design and optimization of drug candidates. Algorithms predict molecular structures and properties, streamlining the drug design process and assessing the likelihood of a molecule being effective and safe.

Virtual screening of large chemical databases is another area where AI excels, reducing the time and cost associated with traditional high-throughput screening methods. AI also predicts drug-drug interactions, helping to identify possible side effects and improve overall drug safety.

In the clinical trial phase, AI analyzes patient data to optimize trial design, identify suitable patient populations, and predict potential outcomes. This leads to more efficient and cost-effective clinical trials. Additionally, AI enables the development of personalized medicine by analyzing individual patient data, tailoring treatment plans based on genetic makeup and other factors.

Throughout these processes, AI algorithms process vast amounts of biomedical data, including research papers, clinical trials, and genetic information, to extract relevant insights. This accelerates the identification of potential drug candidates and provides a comprehensive understanding of disease pathways.

The integration of AI into various stages of drug discovery and development allows for more informed decision-making, cost reduction, and the expedited introduction of new and improved therapies to the market. The collaboration between AI technologies and traditional research methodologies holds the potential to transform the pharmaceutical landscape significantly.