Computational logP Calculations and Determination of Lipophilicity Values of Fluorinated Morphine Derivatives

The addictive effect of opioids impacted 60 million people globally in 2023. This opioid crisis necessitates the development of opioid derivatives that maintain analgesic efficacy while minimizing addictive effects from opioid use. The present work focuses on morphine, a commonly prescribed opioid with high affinity to the mu-opioid receptor (MOR). Morphine binds non-selectively within both healthy and inflamed tissue. By modifying morphine’s structure through fluorination to counteract its non-selective binding, the drug’s pharmacological ability to provide effective pain relief comes with a lower risk of addiction. Specifically, the addition of fluorine to differing beta positions of the main binding site leads to the reduction in pKa (pKa: 6.1-7.83) in comparison to morphine’s pKa (8.2). This reduction in pKa induces specificity to binding within inflamed tissues while avoiding binding within healthy central tissues. We aim to compare the lipophilicity of fluoromorphine derivatives and their connection to pKa and analgesic effects. Lipophilicity, often measured by the partition coefficient (logP), plays a crucial role in morphine's ability to cross lipid membranes, potentially influencing its interaction with MOR. LogP is calculated using logP = (Gibbs Free Energy (water) HFE - Gibbs Free Energy (octanol) SFE)/RT(ln(10)). Calculations are performed in the Keck Computational Research Cluster through the use of Gaussian 16 and Gaussview 6 to model the derivatives computationally. These results will provide insight into potentially increasing the affinity of binding selectively in lower acidity tissues through fluorination of morphine and increased lipophilicity, leading to increased analgesia.