Visualizing and quantifying antimicrobial drug distribution in tissue

The biodistribution and pharmacokinetics of drugs are vital to the mechanistic understanding of their efficacy. Measuring antimicrobial drug efficacy has been challenging as plasma drug concentration is used as a surrogate for tissue drug concentration, yet typically does not reflect that at the intended site(s) of action. Utilizing an image-guided approach, it is feasible to accurately quantify the biodistribution and pharmacokinetics within the desired site(s) of action. We outline imaging modalities used in visualizing drug distribution with examples ranging from in vitro cellular drug uptake to clinical treatment of microbial infections. The imaging modalities of interest are: radio-labeling, magnetic resonance, mass spectrometry imaging, computed tomography, fluorescence, and Raman spectroscopy. We outline the progress, limitations, and future outlook for each methodology. Further advances in these optical approaches would benefit patients and researchers alike, as non-invasive imaging could yield more profound insights with a lower clinical burden than invasive measurement approaches used today.

Time-resolved fluorescence microscopy with phasor analysis for visualizing multicomponent topical drug distribution within human skin

Understanding a drug candidate’s pharmacokinetic (PK) parameters is a challenging but essential aspect of drug development. Investigating the penetration and distribution of a topical drug’s active pharmaceutical ingredient (API) allows for evaluating drug delivery and efficacy, which is necessary to ensure drug viability. A topical gel (BPX-05) was recently developed to treat moderate to severe acne vulgaris by directly delivering the combination of the topical antibiotic minocycline and the retinoid tazarotene to the pilosebaceous unit of the dermis. In order to evaluate the uptake of APIs within human facial skin and confirm accurate drug delivery, a selective visualization method to monitor and quantify local drug distributions within the skin was developed. This approach uses fluorescence lifetime imaging microscopy (FLIM) paired with a multicomponent phasor analysis algorithm to visualize drug localization. As minocycline and tazarotene have distinct fluorescence lifetimes from the lifetime of the skin’s autofluorescence, these two APIs are viable targets for distinct visualization via FLIM. Here, we demonstrate that the analysis of the resulting FLIM output can be used to determine local distributions of minocycline and tazarotene within the skin. This approach is generalizable and can be applied to many multicomponent fluorescence lifetime imaging targets that require cellular resolution and molecular specificity.

Virtual Screening for Ligand Discovery at the σ1 Receptor

The σ₁ receptor is a transmembrane protein implicated in several pathophysiological conditions, including neurodegenerative disease (J. Pharmacol. Sci.2015127 (1), 1729), drug addiction (Behav. Pharmacol.201627 (2-3 Spec Issue), 10015), cancer (Handb. Exp. Pharmacol.2017244237308), and pain (Neural Regener. Res.201813 (5), 775778). However, there are no high-throughput functional assays for σ₁ receptor drug discovery. Here, we assessed high-throughput structure-based computational docking for discovery of novel ligands of the σ₁ receptor. We screened a library of over 6 million compounds using the Schrödinger Glide package, followed by experimental characterization of top-scoring candidates. 77% of tested candidates bound σ₁ with high affinity (K_D < 1 μM). These include compounds with high selectivity for the σ₁ receptor compared to the genetically unrelated but pharmacologically similar σ₂ receptor, as well as compounds with substantial crossreactivity between the two receptors. These results establish structure-based virtual screening as a highly effective platform for σ₁ receptor ligand discovery and provide compounds to prioritize in studies of σ₁ biology.

Quantifying Inflammatory Response and Drug-Aided Resolution in an Atopic Dermatitis Model with Deep Learning

Noninvasive quantification of dermal diseases aids efficacy studies and paves the way for broader enrollment in clinical studies across varied demographics. Related to atopic dermatitis, accurate quantification of the onset and resolution of inflammatory flare ups in the skin remains challenging because the commonly used macroscale cues do not necessarily represent the underlying inflammation at the cellular level. Although atopic dermatitis affects over 10% of Americans, the genetic underpinnings and cellular-level phenomena causing the physical manifestation of the disease require more clarity. Current gold standards of quantification are often invasive, requiring biopsies followed by laboratory analysis. This represents a gap in our ability to diagnose and study skin inflammatory disease as well as develop improved topical therapeutic treatments. This need can be addressed through noninvasive imaging methods and the use of modern quantitative approaches to streamline the generation of relevant insights. This work reports the noninvasive image-based quantification of inflammation in an atopic dermatitis mouse model on the basis of cellular-level deep learning analysis of coherent anti-Stokes Raman scattering and stimulated Raman scattering imaging. This quantification method allows for timepoint-specific disease scores using morphological and physiological measurements. The outcomes we show set the stage for applying this workflow to future clinical studies.