Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning

Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy

Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.

Laser-assisted topical delivery of vismodegib reduces hedgehog gene expression in human basal cell carcinomas in vivo

BACKGROUND

Systemically delivered hedgehog inhibitors including vismodegib and sonidegib are widely used to treat basal cell carcinomas (BCCs). Ablative fractional laser (AFL)-assisted topical delivery of vismodegib has been demonstrated in preclinical studies. The aim of this explorative clinical study was to evaluate intratumoral vismodegib concentrations and effect on hedgehog pathway gene expression following AFL-assisted topical vismodegib delivery to BCCs.

METHODS

In an open-label clinical trial, 16 nodular BCCs (in n = 9 patients) received one application of CO2 -AFL (40 mJ/microbeam, 10% density) followed by topical vismodegib emulsion. After 3-4 days, vismodegib concentrations in tumor biopsies (n = 15) and plasma were analyzed and compared with samples from patients receiving oral treatment (n = 3). GLI1, GLI2, PTCH1, and PTCH2 expression was determined by quantitative polymerase chain reaction (n = 7) and GLI1 additionally by in situ hybridization (n = 3).

RESULTS

Following AFL-assisted topical administration, vismodegib was detected in 14/15 BCCs and reached a median concentration of 6.2 µmol/L, which compared to concentrations in BCC tissue from patients receiving oral vismodegib (9.5 µmol/L, n = 3, p = 0.8588). Topical vismodegib reduced intratumoral GLI1 expression by 51%, GLI2 by 55%, PTCH1 and PTCH2 each by 73% (p ≤ 0.0304) regardless of vismodegib concentrations (p ≥ 0.3164). In situ hybridization demonstrated that GLI1 expression was restricted to tumor tissue and downregulated in response to vismodegib exposure.

CONCLUSION

A single AFL-assisted topical application of vismodegib resulted in clinically relevant intratumoral drug concentrations and significant reductions in hedgehog pathway gene expressions.