Improving the assessment of the probability of success in late stage drug development.

There are several steps to confirming the safety and efficacy of a new medicine. A sequence of trials, each with its own objectives, is usually required. Quantitative risk metrics can be useful for informing decisions about whether a medicine should transition from one stage of development to the next. To obtain an estimate of the probability of regulatory approval, pharmaceutical companies may start with industry-wide success rates and then apply to these subjective adjustments to reflect program-specific information. However, this approach lacks transparency and fails to make full use of data from previous clinical trials. We describe a quantitative Bayesian approach for calculating the probability of success (PoS) at the end of phase II which incorporates internal clinical data from one or more phase IIb studies, industry-wide success rates, and expert opinion or external data if needed. Using an example, we illustrate how PoS can be calculated accounting for differences between the phase II data and future phase III trials, and discuss how the methods can be extended to accommodate accelerated drug development pathways.

N-Methylated peptide inhibitors of beta-amyloid aggregation and toxicity. Optimization of the inhibitor structure.

The key pathogenic event in the onset of Alzheimer's disease (AD) is believed to be the aggregation of the beta-amyloid (Abeta) peptide into toxic oligomers. Molecules that interfere with this process may therefore act as therapeutic agents for the treatment of AD. N-Methylated peptides (meptides) are a general class of peptide aggregation inhibitors that act by binding to one face of the aggregating peptide but are unable to hydrogen bond on the other face, because of the N-methyl group replacing a backbone NH group. Here, we optimize the structure of meptide inhibitors of Abeta aggregation, starting with the KLVFF sequence that is known to bind to Abeta. We varied the meptide length, N-methylation sites, acetylation, and amidation of the N and C termini, side-chain identity, and chirality, via five compound libraries. Inhibitor activity was tested by thioflavin T binding, affinity chromatography, electron microscopy, and an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide toxicity assay. We found that inhibitors should have all d chirality, have a free N terminus but an amidated C terminus, and have large, branched hydrophobic side chains at positions 1-4, while the side chain at position 5 was less important. A single N-methyl group was necessary and sufficient. The most active compound, d-[(chGly)-(Tyr)-(chGly)-(chGly)-(mLeu)]-NH(2), was more active than all previously reported peptide inhibitors. Its related non-N-methylated analogues were insoluble and toxic.

Design strategies for anti-amyloid agents.

Numerous diseases have been linked to a common pathogenic process called amyloidosis, whereby proteins or peptides clump together in the brain or body to form toxic soluble oligomers and/or insoluble fibres. An attractive strategy to develop therapies for these diseases is therefore to inhibit or reverse protein/peptide aggregation. A diverse range of small organic ligands have been found to act as aggregation inhibitors. Alternatively, the wild-type peptide can be derivatised so that it still binds to the amyloid target, but prevents further aggregation. This can be achieved by adding a bulky group or charged amino acid to either end of the peptide, or by incorporating proline residues or N-methylated amide groups.