Good Clinical Practice GCP
Good Clinical Practice (GCP) guidelines
GCP has two important goals: protection of the subject, and protection of the data which is possible only through adequate training of all concerned staff. An independent audit is needed to ensure compliance to the standards. US FDA acts as enforcement officers. As they are less flexible unconventional approaches have no scope. European agencies are more flexible but they need to be convinced.
The Declaration of Helsinki issued by world medical association insists that all research subjects must be fully informed about the nature and risks of a clinical trial. Protocol design is a responsibility of the investigator and need to be assessed by the concerned ethical committee. The principal investigator has an overall responsibility over the entire project.
Informed consent which includes signing a consent form and a recruiting interview preferably by the physician is extremely important. Though not absolutely necessary it is a good practice to define and describe in writing how all aspects of clinical research are to be conducted. These standard operating procedures (SOPs) must be followed during the execution of the research.
Today I learnt few concepts in protein expression which I found very interesting. As we all know, because of the redundancy of the triplet code, it is possible to preserve aminoacid sequence coding while varying the nucleic acid code. This can even happen as a silent mutation. However the availability of corresponding tRNA for each triplet varies from species to species. The translational efficiency of each code may be different (some times to a very great extend) though all may code for the same product. All silent mutations may not be silent after all.
Few codon pairs exist in relative abundance which may act as translation pause sites and slow down translational process. The tRNAs that bind during the translation of such a biased pair appear somehow incompatible. These codon pairs vary from species to species.
These points need to be considered while designing a gene for expression systems. Designing and implementing an algorithm to optimize the gene with respect to the above points can be a good bioinformatics project.
Today I learned about a concept (new to me) called gain of function mutation. A mutation in Loricin gene (LOR 730insG) leads to a frame-shift and delayed termination, thus elongating the protein by 22 amino acids. And changing the Gly/Lys-rich domain into an Arg/Leu-rich terminal domain. Instead of being incorporated into the cell envelope, the mutant loricin is translocated into the neucleus as the mutant C-terminus acquires a new function of a nuclear targeting sequence.
The authors have named the resulting phenotype as honeycomb palmoplantar keratoderma with ichthyosis with occasional features like pseudoainhums, prominent knuckle pads and collodion baby. This is however different from Vohwinkel syndrome (hearing impairment but no ichthyosis) and Olmsted syndrome (severe mutilation and periorificial keratotic plaques). The chapter on hereditary PPKs is already a nightmare for dermatology post graduates with umpteen syndromes. The recent advances make it no better.
M.M. Gedicke et al. Palmoplantar keratoderma with mutation in loricin. Brit Journal of Dermatol 2006;154:167-171
Proteinases play an important role in conditions associated with inflammatory desquamation of the skin like psoriasis, atopic dermatitis and netherton syndrome. Proteinase inhibitors keep their activity under check. Today I read an article  about such an inhibitor called LEKTI (lympho-epithelial Kazal-type-related inhibitor) and the corresponding gene SPINK5, mutations of which is responsible for Netherton syndrome. Authors found that domain 6 and domain 15 inhibit 2 key serine proteinases called hK5 and hK7. They also found that domain 15 with 3 disulphide bonds (and not domain 6) also inhibit plasmin. Plasmin is important in the pathogenesis of pemphigus vulgaris which basically is an autoimmune disease . Hence LEKTI domain 15 may be useful in the treatment of pemphigus vulgaris too. The relevance of the fact that drugs with sulfhydryl groups induce pemphigus with respect to the above finding can be explored.
Interested in further exploring LEKTI bioinformatically?
- T. Egelrud et al. hK5 and hK7, two serine proteinases abundant in human skin, are inhibited by LEKTI domain 6. British Journal of Dermatology 2005;153:1200-1203.
- Naito K, Morioka S, Nakajima S, Ogawa H. Proteinase inhibitors block formation of pemphigus acantholysis in experiments models of neonatal mice and skin explants: effects of synthetic and plasma proteinase inhibitors on pemphigus acantholysis. J Invest Dermatol 1989; 93: 173-7.
DSG3 BJD 2006 Jan 15
DSG3 (BJD 2006 Jan 154 pp67-71)
I was always interested in doing some bioinformatics project on basement membrane zone molecules. Authors have studies various DSG3 SNPs and found two different haplotypes in UK and Indian pemphigus vulgaris patients. Authors have suggested further investigation of this gene.
Structure of GPCRs (PLoS Comp Biol Feb 2006 2(2) p 88-99
Feb 2006 PLOS Computational Biology journal has an interesting article about structure prediction of G Protein – Coupled Receptors. Authors have employed the new threading assembly refinement (TASSER) method to predict the structures for all 907 putative GPCRs out of which at least 820 is supposed to have correct folds. The structures are available for noncommercial use from the university website in my links database. It may be useful for my MC1R study too as GRK2 and 6 are GPCR kinases involved in MC1R signaling. Time to refine my protocol further.
Ref: – British Journal of Dermatology 2005 153, pp1105-1113
Today I read one interesting article titled “Induction of toll-like receptors by propionibacterium acnes”. Toll-like receptors (TLRs) are recently identified group of receptors with homologues in Drosiphila, important in immediate immunological response. 10 different types of TLRs have been identified which are trans-membrane proteins with a leucine-rich extracellular domain and a cytoplasmic domain, called the TIR domain, analogous to the fruit fly Toll protein. They bind to various bacterial antigens like peptidoglycans, Lipoarabinomannans or lipo polysaccharides. Inflammation in acne is due to induction of TLR-2 and TLR-4.
Is there any similarity with MHCs?
Any role in lepra reactions?
Interested in exploring further?
UIMA SDK from IBM
UIMA SDK from IBM
Today I explored the UIMA SDK from IBM. It is a software system that analyses large volumes of unstructured information, discover, organize and deliver relevant knowledge. An example they have sited as an application of UIMA is to find potential drug interactions by processing millions of medical abstracts. I will try to explore its application in medical diagnostics and find out whether I can use it in some way for my virtual dermatologist I am working on. Probably it has some applications in bioinformatics too!
In this months biopharm I read about the spiralling cost of biomedical research. New drug approvals have flattened out in recent years, particularly for new molecular entities. As far as man power is concerned, over the last few years there has been an unprecedented increase in the number of bioinformaticians and there is no dearth of lead molecules. However clinical research personal failed to catch up and manufacturers are under pressure to find out more efficient ways to develop medical products. I think very soon the initial phases of clinical studies may become computer simulations and the actual human studies will become more targeted.
Today I read one article about 5 untranslated region or 5UTR which was a new concept for me. The translational efficiency of mRNA depends on various factors like the context of start codon, AUGs within 5 UTR and the structural stability of mRNA. The authors tried to find out the 5UTR features influencing the translational efficiency by comparing various High expression and Low expression mRNAs. They prepared a database out of this information called LEADER_RNA and the translational prediction algorithm.
help plss in protein structure prediction
My name is Abhijit Jadhav.I m doin my graduate project in secondary protein structure prediction using Neural networks.I had few doubts and would be very greatful if you ppl could slove it
1.Is there some benchmark problem for secondary protein structure prediction using neural nets?
2.Is there some neural network solution algorithm for protein structure prediction like RBF networks or SVM?