MASTER DRUG INDEX 2012 PDF

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3–4 December Representatives discussed worksharing possibilities in the areas of active substance master files (ASMFs)/drug master files (DMFs). WHO Drug Information Vol. 26, No. 3, landscape in the form of a global network of the network can master this process, it would be The Drug Master File is referred to . documents/TRS/tingrakecoupde.ga 6. Master document, updated February Page 2 of PROTECT: Joan Fortuny (Novartis) from April to April 1Participated during the first .. It also includes information on drug providers, national healthcare.


Master Drug Index 2012 Pdf

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Master document, updated November Country profile document authors: Elena Ballarín (FICF), Mònica Sabaté. (FICF) .. information on drug consumption databases, from governmental agencies in charge of. Info helps to master FDA's list of Drug Master Files (DMFs) and info on Master file for Shared System REMS Submissions: Guidance for Industry (PDF - KB) . Draft Guidance for Industry: Generic Drug User Fee Amendments of The Drug Listing Act of requires registered drug establishments to FDA publishes the listed NDC numbers and the information submitted as part of the , are included on a separate file and will not be updated after June

Of course, the challenge in such work was to discriminate causal from incidental differences. By shedding light on disease-associated genes and pathways, EST profiling began to shift the focus in drug discovery and development from alleviating symptoms to attacking the underlying mechanisms of disease.

Furthermore, this approach substantially expanded the array of potential targets, in turn providing wide-spread optimism that genomics could help resolve the burgeoning pharmaceutical productivity crisis of the early s 6. Accordingly, the late s and early s saw a surge of partnerships between genomics firms that developed proprietary databases of expression data and pharmaceutical companies that had expertise and resources in drug development 4 , 6 , Although numerous factors have been demonstrated to be at fault, one potential contributor was that early genomics transformed the bottleneck of the productivity crisis from target discovery to effective prioritization of numerous candidate targets.

Given that the genomics leads were both plentiful and often of little direct value in terms of elucidating the biological functions of the targets 6 , there was no immediately available basis for prioritization.

Consequently, from to , there was a tenfold increase in research and development spending, and this increase, paired with an inflated number of false positives and growing drug attrition rates, led to an overall continued decline in both pharmaceutical productivity and optimism in this approach Target validation, through either a robust genetic link between the target and the disease or a greater understanding of the role played by the target in the disease aetiology, improved the value of genetic information.

For example, in the AstraZeneca portfolio from to , targets with stronger validation of the biological role of the target in human disease were less likely to fail in clinical development owing to a lack of efficacy Furthermore, a broad analysis identified an increased proportion of targets with direct genetic evidence as the development pipeline progresses through to approval GWAS and drug discovery The rising challenge of clinical trial failures led drug developers to seek information that better identified targets worth pursuing.

This coincided with the publication of the human genome sequence in , followed by the characterization and arrangement of nearly three million single nucleotide polymorphisms SNPs into genome-wide haplotype maps as part of the International HapMap project 18 , both of which provided the tools necessary to begin to understand the relationship between common human genomic variation and complex diseases.

This was primarily accomplished through genome-wide association studies GWAS — an unbiased and cost effective approach to genotyping and comparing much of the common genomic variation of cohorts with particular diseases or traits to that of control populations. Markers that associate with the disease more often than expected indicate the presence of causal variants in the genomic vicinity of the associated variants, which, in theory, could be identified through additional genetic analyses, such as fine mapping or re-sequencing.

From a drug discovery standpoint, identifying genetic determinants of common disease through GWAS provided valuable knowledge about the genetic architecture of disease and potential pointers to underlying causative mechanisms and engendered the promise of discovering better candidate targets for common, complex diseases such as diabetes, cancer and autoimmune conditions 19 , From to , nearly 2, significantly associated loci of complex diseases and traits were discovered, which translated to nearly 2, new potential pharmacological leads Some of these loci were found to be associated with multiple diseases, such as SNPs discovered at the IL23R locus that were associated with several autoimmune conditions including psoriasis, inflammatory bowel disease and ankylosing spondylitis 22 — These pleiotropic genes were of particular interest given their potential as targets for blockbuster drugs treating several diseases Despite these efforts, the vast majority of well-accepted disease-associated loci have yet to be biologically explained, which has hindered the widespread implementation of GWAS findings in drug discovery pipelines.

GWAS data often fail to uncover causal variants and, in many cases, even fail to implicate specific genes. GWAS findings often implicate numerous candidate genomic regions, which can include nearly all the genes close to the identified signal: as such, concrete guidance for drug development is limited.

This finding was highly significant and replicated across different ethnic populations and age groups with an overall estimated 1. Although FTO has been the subject of intense investigation to determine its contribution to obesity, a solid causal link has yet to be established, and the roles of other genes under regulatory control of the FTO locus, such as IRX3 and IRX5, have recently been implicated 37 , The challenge in making effective use of most GWAS findings in the context of drug development has been formally explained recently by Pritchard 39 and colleagues in the form of their omnigenic model.

They note that for many human traits and diseases, there are as many as , contributing SNPs that are distributed throughout the genome. In the early days of GWAS, a similar explanation was proposed for the genetics of human height using a strictly orthogonal approach.

Relying on the effect size distribution of height-associated SNPs as a basis for estimating the full distribution of effect size for all height variants, including those that remained undiscovered, it was inferred that as many as 93, independent variants would be required to account for the heritability of height The model suggests that the sum of these numerous small effects on core genes could substantially alter disease expression.

Drug development in the era of precision medicine

In addition, although information on peripheral gene involvement from GWAS could be useful in determining individualized disease risks, drug discovery and development would instead clearly benefit far more from the firm identification and targeting of the strong-effect core genes.

To the extent that this model is accurate, it provides a compelling explanation of why the vast majority of GWAS findings have had little relevance to drug discovery. Recent technological advances have increased the efficacy and speed and reduced the cost of high-throughput sequencing, which has improved our ability to characterize and interpret human genomic variation — particularly rare variation. This knowledge thereby builds confidence in the rationale for targeting that gene product for the treatment of a more common human disease, rather than relying on information gained from less predictive animal or cellular models.

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In , gain-of-function mutations in PCSK9 were identified in French families with autosomal dominant hypercholesterolaemia, a condition associated with an increase in the risk of early-onset cardiovascular disease Loss-of-function mutations were also subsequently identified in association with both low plasma levels of low-density lipoprotein cholesterol LDL-C in African American and white cohorts and an overall decreased risk of coronary events with no notable health problems 43 — Functional analyses demonstrated that PCSK9 is a hepatic secretory protein that can enter the circulation and bind to LDL receptors the primary source for clearance of circulating cholesterol , ultimately mediating LDL receptor endocytosis and subsequent degradation 47 , The combination of these genetic, phenotypic and molecular findings served as the basis for pursuing PCSK9 as a drug target with the rationale that PCSK9 inhibition would result in a surplus of available LDL receptors, thereby reducing plasma LDL-C levels and lowering the risk of adverse cardiac events A series of clinical trials were conducted using humanized monoclonal antibodies against PCSK9 in treating populations of patients stratified based on cholesterol levels, cardiovascular disease risks, use of other lipid-modifying enzymes and duration of treatment, and the US Food and Drug Administration FDA has since approved two monoclonal antibodies alirocumab and evolocumab for the treatment of high cholesterol not adequately controlled by statins or diet 49 see Further information.

Thus far, results from the FOURIER trial support a lower incidence of adverse cardiovascular events in patients with atherosclerotic cardiovascular disease treated with evolocumab while on a statin therapy, with no apparent increase in other adverse outcomes such as new-onset diabetes or neurocognitive side effects The promise of this approach in identifying and validating drug targets with overall higher chances for successful clinical trial outcomes and lower drug attrition rates has seemingly revived the excitement for genetics in drug discovery 41 , 51 , In , Amgen acquired the Iceland-based genomics company deCODE and have since published a set of nearly 7, rare loss-of-function mutations in a large Icelandic population for use in their drug discovery pipeline This initiative has already revealed a role for ANGPTL4 in coronary artery disease, as loss-of-function mutations are associated with good lipid profiles and a lower risk of coronary artery disease These findings mirror the effects of monoclonal antibodies in animal models 54 , further supporting ANGPTL4 as a candidate drug target.

Although this overall approach to drug discovery is certainly compelling and has led to enthusiastic reviews, it remains unclear how many of these pointers human genomic variation will ultimately provide. Hence, it seems likely that such pointers to generally useful medicines, regardless of underlying causes of disease in individual patients, may be fairly rare.

National Drug Code Directory

Box 1 Gene to drug: SOST and SCN9A In the late s, before the advent of next-generation sequencing, Brunkow and colleagues examined 22 families from an Afrikaner population in South Africa who had sclerosteosis, a rare, severe sclerosing skeletal dysplasia that results in massive bone overgrowth. Through genetic mapping and positional cloning, they identified mutations in a novel gene, SOST All changes must be reported as amendments.

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Recently, API supply chains are being increasingly globalized, and many countries import APIs from foreign manufacturers such as China and India [ 14 — 16 ].

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