For us, lead optimization is much more than optimizing potency and selectivity, it's about turning hits into future medicines.
Our goal is to put overwhelming effort into our lead optimization projects so that our partners can enter their compounds into development with confidence. To accomplish this, we first understood why candidates fail in pre-clinical and clinical development and then found ways to assay for many of these failures earlier on. Now we include this "developability" strategy into our Multi-Parametric Lead Optimization efforts using a parallel process approach.
The result: better candidates in less time.
Multi-Parametric Lead Optimization
Features of our Multi-Parametric Lead Optimization Service are:
- State of the art computational chemistry suites for ADMET and developability prediction, extensively and constantly included in our Multi-Parametric Lead Optimization
- Right balance between a parallel high throughput approach and iterative organic chemistry to exploit the full range of chemical possibilities
- QC-tested compounds with >90% purity determined by LC-MS
- In vitro developability profiling, in parallel, to address potency, selectivity, metabolic stability and biotransformation studies, P450 interactions, hERG binding, biomembrane permeability, solubility, protein binding, etc
- In vivo preliminary PK parameters determination
- Pharmaceutical industry perspective. We do not focus just on chemistry, we focus on the big picture, which is about creating new chemical entities that have better chances of becoming blockbuster medicines. We create patentable molecules that will better survive development, and will outsell the competition
- Once the initial design-synthesis-screen round is completed, all information is gathered and used to refine the strategy for a subsequent round, and this cycle continues until we identify the best possible candidate(s)
- Our commitment to the customer does not stop here. We retain all information gathered during the optimization process and, should one of our candidates fail in development we can use the downstream data to quickly create a more suitable replacement