Product Life Cycle Management for Pharmaceutical Innovation
Bibliographic record
Abstract
Background: Continuous innovation is the unique feature of a pharmaceutical industry and is also prerequisite for sustainment of this industry. However, the dipping number of new molecules, rising costs of drug development, heightened health authority scrutiny and increased competition from generic industry indicate that the innovation is endangered. Methodology: The methodology used is a comparative study on the basis of original empirical research. More specifically, the part of examination facts and regulation has been written by conducting empirical research on current international and national resources concerning the subject from books, various Guidelines, Rules and regulations, Articles, Published Reports and internets. Results: The importance of innovation has been fairly realized by the industry and regulatory authorities as evident from the rise in number of new molecules approved by US FDA in 2014. To safeguard innovation, Product Lifecycle Management (PLM) should be incorporated in the business models of a pharmaceutical company. It not only helps to maximize lifecycle of the product but also to improve the product development processes, make better business decisions and to deliver greater value to consumers. Conclusion: A pharmaceutical product’s life is always complex & unique in nature and can be described in five distinct phases- development phase, approval phase, introduction phase, commercialization & quality management phase and decline phase. Number of strategies to be applied at each stage and the strategies are generally coupled with regulations, and the choice of strategy may vary on country to country. In present manuscript, the scope of various PLM strategies for innovators in four different countries namely, USA, EU, Canada and India has been discussed. This may guide the innovators to have competency to uphold the basic necessity for their survival, i.e. innovation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".