Preclinical toxicology and safety pharmacology of the first-in-class GADD45ß/MKK7 inhibitor and clinical candidate, DTP3.

Aberrant NF-κB activity drives oncogenesis and cell survival in multiple myeloma (MM) and many other cancers. However, despite an aggressive effort by the pharmaceutical industry over the past 30 years, no specific IκBα kinase (IKK)ß/NF-κB inhibitor has been clinically approved, due to the multiple dose-limiting toxicities of conventional NF-κB-targeting drugs. To overcome this barrier to therapeutic NF-κB inhibition, we developed the first-in-class growth arrest and DNA-damage-inducible (GADD45)ß/mitogen-activated protein kinase kinase (MKK)7 inhibitor, DTP3, which targets an essential, cancer-selective cell-survival module downstream of the NF-κB pathway. As a result, DTP3 specifically kills MM cells, ex vivo and in vivo, ablating MM xenografts in mice, with no apparent adverse effects, nor evident toxicity to healthy cells. Here, we report the results from the preclinical regulatory pharmacodynamic (PD), safety pharmacology, pharmacokinetic (PK), and toxicology programmes of DTP3, leading to the approval for clinical trials in oncology. These results demonstrate that DTP3 combines on-target-selective pharmacology, therapeutic anticancer efficacy, favourable drug-like properties, long plasma half-life and good bioavailability, with no target-organs of toxicity and no adverse effects preclusive of its clinical development in oncology, upon daily repeat-dose administration in both rodent and non-rodent species. Our study underscores the clinical potential of DTP3 as a conceptually novel candidate therapeutic selectively blocking NF-κB survival signalling in MM and potentially other NF-κB-driven cancers.

In silico prediction of ADME properties: are we making progress?

The use of computational models in the prediction of ADME properties of compounds is growing rapidly in drug discovery as the benefits they provide in throughput and early application in drug design are realized. In addition, there is an increasing range of models available, as model builders have advanced from the first-generation' models, which were predominantly focused on solubility, absorption and metabolism, to include models of other optimization factors such as HERG, glucuronyl transferase and drug transport proteins. This widening interest is now driving demand for developments in the component elements of model building, namely higher quality datasets, better molecular descriptors and more computational power, and the quality of models is improving rapidly as a consequence. Models generally have very high throughput and can be used with virtual structures. As a consequence, they can generate large quantities of data on large numbers of compounds. Thus, one consequence of the wider choice of models, coupled with their high throughput, is a growing need to integrate their output into collective analyses of molecules against pre-set criteria. This article comments on some of the recent developments in ADME models, and highlights the importance of integrating the data to aid compound selection in drug discovery projects.

The emerging importance of predictive ADME simulation in drug discovery.

Absorption, distribution, metabolism and excretion (ADME) studies, are widely used in drug discovery to optimize the balance of properties necessary to convert leads into good medicines. However, throughput using traditional methods is now too low to support recent developments in combinatorial and library chemistry, which have generated many more molecules of interest. To the more enlightened practitioners of ADME science, this situation is generating both the problem and the solution: an opportunity is now forming, with the use of higher throughput ADME screens and computational models, to access this wide chemical diversity and to dissect out the rules that dictate a pharmacokinetic or metabolic profile. In the future we could see ADME properties designed-in from the first principles in drug design.