Novel prodrugs for targeting diagnostic and therapeutic radionuclides to solid tumors

Tailoring the AIE Chromogen 2-(2-Hydroxyphenyl)benzothiazole for Use in Enzyme-Triggered Molecular Brachytherapy

A targeted strategy for treating cancer is antibody-directed enzyme prodrug therapy, where the enzyme attached to the antibody causes conversion of an inactive small-molecule prodrug into an active drug. A limitation may be the diffusion of the active drug away from the antibody target site. A related strategy with radiotherapeutics entails enzymatically promoted conversion of a soluble to insoluble radiotherapeutic agent, thereby immobilizing the latter at the target site. Such a molecular brachytherapy has been scarcely investigated. In distinct research, the advent of molecular designs for aggregation-induced emission (AIE) suggests translational use in molecular brachytherapy. Here, several 2-(2-hydroxyphenyl)benzothiazole substrates that readily aggregate in aqueous solution (and afford AIE) were elaborated in this regard. In particular, (1) the 2-(2-hydroxyphenyl) unit was derivatized to bear a pegylated phosphodiester that imparts water solubility yet undergoes enzymatic cleavage, and (2) a p-phenol unit was attached to the benzo moiety to provide a reactive site for final-step iodination (here examined with natural abundance iodide). The pegylated phosphodiester-iodinated benzothiazole undergoes conversion from aqueous-soluble to aqueous-insoluble upon treatment with a phosphatase or phosphodiesterase. The aggregation is essential to molecular brachytherapy, whereas the induced emission of AIE is not essential but provides a convenient basis for research development. Altogether, 21 compounds were synthesized (18 new, 3 known via new routes). Taken together, blending biomedical strategies of enzyme prodrug therapy with materials chemistry concerning substances that undergo AIE may comprise a step forward on the long road toward molecular brachytherapy.

Computational and biological evaluation of quinazolinone prodrug for targeting pancreatic cancer

Our concept of enzyme-mediated cancer imaging and therapy aims to use radiolabeled compounds to target hydrolases over-expressed on the extracellular surface of solid tumors. A data mining approach identified extracellular sulfatase 1 (SULF1) as an enzyme expressed on the surface of pancreatic cancer cells. We designed, synthesized, and characterized 2-(2'-sulfooxyphenyl)-6-iodo-4-(3H)-quinazolinone (IQ(2-S)) as well as its radioiodinated form ((125) IQ(2-S)) as a prodrug with potential for hydrolysis by SULF1. IQ(2-S) was successfully docked in silico into three enzymes - homolog of SULF1, alkaline phosphatase, and prostatic acid phosphatase. The incubation of (125) IQ(2-S) and (125) IQ(2-P) with the three enzymes in solution confirms the docking results and enzyme selectivity for the analogs. The hydrolysis of both radioactive compounds produces the water-insoluble, fluorescent product 2-(2'-hydroxyphenyl)-6-[(125) I]iodo-4-(3H)-quinazolinone ((125) IQ(2-OH)). The in vitro incubation of (127) IQ(2-S) and (127) IQ(2-P) with pancreatic, ovarian, and prostate cancer cells expressing studied hydrolases also results in their hydrolysis and the precipitation of (127) IQ(2-OH) fluorescent crystals on the cell surface. To our knowledge, these findings are the first to report the targeting of a radioactive substrate to SULF1 and that this prodrug may be potentially useful in the imaging ((123) I/(124) I/(131) I) and radiotherapy ((131) I) of pancreatic cancer.

Human placental alkaline phosphatase-mediated hydrolysis correlates tightly with the electrostatic contribution from tail group

Human placental alkaline phosphatase has been identified as a hydrolase that is significantly overexpressed on the surface of various solid tumor cells, and is therefore a suitable prodrug design target for non-invasive cancer imaging and therapy. Structure-based prediction of enzymatic activities is essential for rational prodrug design. We have been probing the catalytic proficiency--(k(cat) /K(M) )/k(w)--of placental alkaline phosphatase toward several widely diverse substrate structures experimentally and correlating these results to in silico predictions that are based on the free energy estimates obtained from docking of each substrate structure with placental alkaline phosphatase. We have found that electrostatic contribution from the tail group is the most crucial factor to determine the catalytic efficiencies of the substrates. The electrostatic contribution and the total binding energy of the tail group are well correlated with catalytic efficiencies (R² = 0.79 and 0.89, respectively). However, hydrophobic contribution from the tail group does not correlate with the catalytic efficiencies (negative correlation, R² = 0.27). This supports the prior hypothesis stating that alkaline phosphatase-mediated differential hydrolysis of its substrates is attributable to the differential interactions with the tail group, determined by the electrostatic contributions from the non-bridging oxygen atoms. Calculation of the electrostatic potentials within the active site of human placental alkaline phosphatase also suggests that the local positive electrostatic environment may account for its capability to distinguish various substrates. Our study is likely to have immediate implications in the design of prodrugs against human placental alkaline phosphatase and other esterases overexpressed by human tumor cells.

Integrated bioinformatics analysis for cancer target identification

The exponential growth of high-throughput Omics data has provided an unprecedented opportunity for new target identification to fuel the dried-up drug discovery pipeline. However, the bioinformatics analysis of large amount and heterogeneous Omics data has posed a great deal of technical challenges for experimentalists who lack statistical skills. Moreover, due to the complexity of human diseases, it is essential to analyze the Omics data in the context of molecular networks to detect meaningful biological targets and understand disease processes. Here, we describe an integrated bioinformatics analysis strategy and provide a running example to identify suitable targets for our in-house Enzyme-Mediated Cancer Imaging and Therapy (EMCIT) technology. In addition, we go through a few key concepts in the process, including corrected false discovery rate (FDR), Gene Ontology (GO), pathway analysis, and tissue specificity. We also describe popular programs and databases which allow the convenient annotation and network analysis of Omics data. We provide a practical guideline for researchers to quickly follow the protocol described and identify those targets that are pertinent to their work.