Optimizing Drug Development Programs: Type 2 Diabetes Case Study.

Recently, consideration was given to the impact of dose selection strategies in phase IIb on the overall success of drug development programs. A natural next step is to simultaneously optimize design aspects of both phase IIB and phase III. We used type 2 diabetes as an example, including realistic regulatory and commercial scenarios for this indication. The expected net present value (eNPV) has been selected as the primary outcome because it naturally accommodates optimization, providing an explicit trade-off between the probability of success (PoS) and time delays and trial costs. Our findings are that larger studies and/or implementation of an adaptive design over a fixed design in phase IIb provide more precise dose selection and reduce the bias of treatment effects and uncertainty in the estimated eNPV within the range of sample sizes that we examined. Developers also have to ensure that dose selection criteria are consistent with development strategy and objectives.

Haplotype homozygosity and derived alleles in the human genome.

Haplotype-based techniques are being used to estimate the relative age of alleles--particularly in screening loci for signals of recent positive selection--but does this approach capture even coarse age differences? Using simulations and empirical data from the International HapMap Project, we show that a simple pairwise metric of haplotype homozygosity gives significantly higher mean values for human single-nucleotide-polymorphism alleles that appear to be derived than for those that appear to be ancestral, as determined by comparison with the chimpanzee genome. Our results support the use of haplotype-based techniques, such as extended haplotypic homozygosity, to assess the age of alleles.

Screening for recently selected alleles by analysis of human haplotype similarity.

There is growing interest in the use of haplotype-based methods for detecting recent selection. Here, we describe a method that uses a sliding window to estimate similarity among the haplotypes associated with any given single-nucleotide polymorphism (SNP) allele. We used simulations of natural selection to provide estimates of the empirical power of the method to detect recently selected alleles and found it to be comparable in power to the popular long-range haplotype test and more powerful than methods based on nucleotide diversity. We then applied the method to a recently selected allele--the sickle mutation at the HBB locus--and found it to have a signal of selection that was significantly stronger than that of simulated models both with and without strong selection. Using this method, we also evaluated >4,000 SNPs on chromosome 20, indicating the applicability of the method to regional data sets.

Accuracy of haplotype reconstruction from haplotype-tagging single-nucleotide polymorphisms.

Many investigators are now using haplotype-tagging single-nucleotide polymorphism (htSNPs) as a way of screening regions of the genome for association with disease. A common approach is to genotype htSNPs in a study population and to use this information to draw inferences about each individual's haplotypic makeup, including SNPs that were not directly genotyped. To test the validity of this approach, we simulated the exercise of typing htSNPs in a large sample of individuals and compared the true and inferred haplotypes. The accuracy of haplotype inference varied, depending on the method of selecting htSNPs, the linkage-disequilibrium structure of the region, and the amount of missing data. At the stage of selection of htSNPs, haplotype-block-based methods required a larger number of htSNPs than did unstructured methods but gave lower levels of error in haplotype inference, particularly when there was a significant amount of missing data. We present a Web-based utility that allows investigators to compare the likely error rates of different sets of htSNPs and to arrive at an economical set of htSNPs that provides acceptable levels of accuracy in haplotype inference.