“In recent history, computational chemistry and in silico ADMET modeling has been a hot topic in drug development, and our focus has been to expand on in silico research,” said Lobell.
Most of Bayer Healthcare’s in silico ADMET methods have been developed in-house, which has been advantageous to its drug development on multiple levels, reported Lobell. “The consistency of working closely with and collaborating with scientists in-house guarantees consistent results. The scientists are familiar with the methodology and the endpoints we are working with, and in the end, working with in-house data tends to generate consistency.”
Lobell presented his ADMET Traffic Lights system for in silico ADMET characterization of HTS hit structures and its application to kinase inhibitors. “The biggest challenge in the in silico field is making an accurate model,” he said. “And for that, it’s not enough to generate data; you need to be able to access that data and make sense of it.”
The system is one that Lobell invented, developed, and helped implement at Bayer. “The primary purpose of the in silico ADMET Traffic Lights is not that it’s an automatic filter but rather an aid to help MedChem experts analyze and prioritize HTS hit sets with regard to potential ADMET-related liabilities.
“We have built our scoring system on ADMET-relevant parameters, which are well known to medicinal chemists and can be addressed by structural modifications. Also the scoring system is simple. The generated output is easy to interpret due to the intuitive traffic light color scheme and a scoring system in the convenient range from zero to ten,” said Lobell.
Genkyotex (www.genkyotex.com) uses multiple computer models in order to develop drugs that block enzymes that produce oxygen radicals.
“We are currently optimizing three chemical series for hypertension, myocardial ischemia/reperfusion, and lung fibrosis,” said Cedric Merlot, head of knowledge management and computational chemistry. “Initially we plan to partner one program, and we hope that we will take the other two to approval ourselves.”
The company’s seed funds are limited, thus resources cannot be wasted on synthesizing and testing compounds that will obviously not make it in ADME and safety. Early-stage optimization is vital, Merlot added. “We take full advantage of ADME prediction and computational toxicology to make sure that the drugs we are testing in vivo are worth it. We have access to in silico ADME profiles, which allows us to focus on compounds that have a greater chance to be bioavailable. Then, and most importantly, we are using computational toxicology to identify potential liabilities.”
Merlot’s team tries to make the best use of computers for supporting its projects. “We have a partner that helped us a lot. Drug Design Technologies (www.drugdesigntech.com) provided us with a corporate database and efficient prediction tools for early ADME and safety assessment,” said Merlot.
“Historically, in silico models have been used to filter out compounds that were predicted to be bad, sometimes filtering too much and resulting in the loss of valuable leads,” explained Merlot. “What we are doing is using them for asking the right question.”
His group runs a large panel of models on each of their chemical series. Positive results are then followed up with in vitro assays to confirm the prediction. Then, this information is interpreted in the context of a project. “Knowing the therapeutic area or the disease, we can say whether this liability is a safety concern or not,” said Merlot. “It increases our confidence in the chemical series.”
Current prediction models are worth using in drug discovery, concluded Merlot. “Probably no model is better than the others; it is just about finding the one(s) that will answer the question.”