DNAprint Genomics (DNAG)

[sam1933]

<Why are people here??>

*Today’s drugs are overly expensive, exhibit unacceptable rates of adverse events (disturbingly, not often apparent during clinical trials), take too long to reach the market and ignore smaller, yet profound, disease markets.
*Most of these problems are due to a broken pharma culture, which seeks to serve short-term interests at the expense of their customers and long-term interests.
*Aside from regulatory problems, which are themselves side-effects of the broken pharma culture, most of the problems can be cured by dropping the blockbuster model in favor of a segmented market model, where safer and more powerful and effective medicines are used with specific populations.
*This change will require the application of existing, but also novel, genomics methods.

Why are people here??

*DNAPrint applies novel technologies for stratifying patient populations, using structured-association pharmacogenomics methods with proprietary marker panels and innovative computational modeling.
*Application of these technologies is expected to enable the company to develop more efficient medicines for segmented markets.

Specifically, we expect:

–Smaller clinical trials: smaller clinical trials will be needed as the number of clinical-trial patients will be chosen based upon their genetic predisposition;
–Faster clinical trials: faster clinical trials as the rate of success will be higher since the trial patients will be chosen based upon their genetic predisposition;
–Maximized clinical-trial success: these technologies will maximize clinical-trial success by anticipating factors that could cause failures in drug safety and/or efficacy;
–Increased patient lifespan: DNAPrint Pharmaceuticals Inc. anticipates a reduction in patient mortality since the drug will be given to patients who have a genetic predisposition to respond favorably to the drug;
–Decreased drug side effects: the products will prevent/minimize side effects, minimizing litigation expenses and expensive recalls;
–Increased drug efficacy: the products will have increased effectiveness over current available products;
–Lower costs: lower costs during the clinical-trial phase as savings will be realized by excluding patients who will not respond or who will have an adverse reaction.





DNAPrint’s pharmacogenomics method is based on our understanding of how the genome is not only a blueprint of individuality, but also a record of our ancestral heritage. Drug metabolism and therapeutic effects are fundamental, adaptive functions developed in response to dietary and environmental xenobiotics, which are appreciably different between regions of the world, with different climates, fauna and flora. Human populations arising in such distinct habitats have developed variant systems for coping with them, for example, the highly polymorphic drug-metabolizing enzymes (e.g., CYP family of proteins). These types of adaptations remain embedded in our genomes over generations and are shared within populations as they expand and persist. Thus, determination of genetic ancestry admixture in patient populations, at the level of the individual patient, is an important parameter for better resolution of subpopulations according to proclivity for beneficial and/or adverse drug responses.
Individual admixture proportions are important for our pharmacogenomics approach in two distinct ways:

*To correct for the confounding influence of population structure on association study design (associations not corrected often result through association with elements of population structure that themselves are correlated with response);
*To account for variation in response not explained by loci uncovered from genome and/or xenobiotic metabolism gene scans (perhaps because they are too numerous, or of too low a penetrance for reasonably sized studies).

The company’s ANCESTRYbyDNA™ 2.5 assay quantitatively reports admixture with respect to the most basic level of population structure, which paleoarchelogical, linguistics and genetic data suggest comports largely to the world’s main continents . The Company’s EURODNA™ 1.0 and 2.0 assays assess sub-European elements of population structure, which may be more important for some drugs (e.g., northwestern European, Iberian, southeastern European and so on).

Our individual assessments of biogeographical ancestry are then used as conditioners for pan genome structured association scans to dramatically reduce both type I and II discovery error. For these screens, we make use of automated platforms provided by Beckman (ultra high throughput [UHT] SNPstream) and Illumina (BeadStation). We began by identifying a locus associated with atorvastatin-induced muscle effects, including myalgia and life-threatening rhabdomyolysis [Frudakis ‌ T, Thomas M, Ginjupalli S, Handelin B, Gabriel R, Gomez H: CYP2D6*4 polymorphism is associated with statin-induced muscle effects. Pharmacogenet. Genom. 17(9), 695–707 (2007). ], which caused Bayer’s Baycol (cerivastatin) to be withdrawn from the market in 2003. Another screen was successful in identifying the primary determinant of variable iris color (a model for drug response), and patient ‘classification’ systems developed from this work represent, to our knowledge, the first for predicting a non-Mendelian, quantitative phenotype from DNA sequence information [Frudakis ‌ T, Terravainen T, Thomas M: Multilocus OCA2 genotypes specify human iris colors. Hum. Genet. 122, 311–326 (2007). [CrossRef] [Medline] . However, each phenotype is different, and we expect to incorporate information on multiple levels – protein modifications (proteomics), RNA expression (expression profiling) and DNA variation (pharmacogenomics and population structure) – to stratify responders for the drugs in our pipeline.
Computational modeling
Our platform for simulating biological processes, BioFusion®, puts to work the vast body of knowledge accumulated to date by scientists the world over to provide a road map, so to speak, for the biology underlying response to our drugs. We first acquire extensive knowledge and information about the target biology – identifying, integrating and graphically depicting the current state of relevant knowledge. With this foundation, computer simulation is then carried out using a formalized methodology and simulation language. After the in silico model is created, it is verified and validated to ensure that the behavior of the simulation is representative of the biological processes it has been designed to explore. The simulation is subjected to rigorous verification and validation by benchmarking model behavior against known patterns of biological behavior, and tested for accuracy at each level of detail against available experimental and clinical data. These patterns of behavior are referred to as biologic reference patterns and are selected to represent the known, accepted, paradigmatic behavior of the biological system being modeled. They can include anything from the concentration of protein (or mRNA) produced under a given set of circumstances to the latency and/or likelihood of cell differentiation. If the model fails to meet these benchmarks, it is examined to determine the cause for the failure, model parameters are adjusted and/or new information added to bring the model into compliance with the requirements. The road map that results represents an intelligence dossier, relevant for various comorbidities, competing medications or other conditions and so on that may be relevant to our trial designs and help define our target markets.