Rheingold: An app for cytochrome prediction

Some pretty pictures of detoxification network analysis courtesy of an app I’m developing called ‘Rheingold,’ which takes drug/ supplement/ food queries and returns predicted cytochrome consequences.

In addition, with this client I was able to superimpose their Opus23 genomic data, and as can be seen in the image, the red box for CYP2D6 shows a double whammy: The enzyme is compromised because of the clients genetic mutations, plus one of their meds is inhibiting it even further. This is noteworthy because CYP2D6 is known to metabolize as many as 25% of commonly prescribed drugs, including antidepressants, antipsychotics, analgesics and antitussives, beta adrenergic blocking agents, anti-arrythmics and antiemetics.

Rheingold also supplies predictive data with regard to what might be a consequence of adding addition agents to the client’s protocol. All in all a very satisfying coding experience. In this example, the client may well have a problem if prescribed a beta-blocker for hypertension, or codeine for a cough. Both would leave the body rather slowly, increasing chances for a drug side-effect.

The gene for CYP2D6 is highly polymorphic (variable between individuals). Certain alleles result in the ‘poor metabolizer’ phenotype, characterized by a decreased ability to metabolize the enzyme’s substrates. Some individuals with the poor metabolizer phenotype have no functional protein since they carry 2 null alleles whereas in other individuals the gene is absent. Individuals with the ultra-rapid metabolizer phenotype can have 3 or more active copies of the gene.

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