Release date: 2017-04-05 Developing new drugs is a long and inefficient job. The data shows that less than 12% of all drugs entering the clinical trial phase are finally available for sale, and the average R&D cost of a new drug is as high as $2.6 billion. Drug developers need to test a variety of different compounds and chemicals, and the erroneous attempts in this trial cost too much time and money. Because there are so many molecules to test, developers have to use a pipetting robot to test thousands of variants at a time, and then select the most effective variants for animal models or cell culture experiments, hoping that some of them will eventually enter the human clinical trial phase. As the cost of trial and error is too high, more and more drug developers are turning to computers and artificial intelligence, hoping to use this technology to narrow down the range of potential drug molecules, thereby saving time and money for subsequent testing. In order to identify genes encoding proteins that have great potential as drug targets, these vendors pin their hopes on algorithms. At present, some new algorithm models (including recently published in Science Translational Medicine) add a new level of complexity to narrow the range of related proteins, drugs and clinical data to better predict which genes are most likely Let protein and drugs combine. "Many causes can lead to drug development failures," said genetic epidemiologist Aroon Hingorani. "However, one of the main reasons is that it fails to select the right target for the disease." A drug may be in cells, tissues, and animals. Early experiments in the model showed preliminary prospects, but these early experiments were often too simplistic and rarely used randomized blind experiments for comparison. Scientists use these results to predict which proteins can be used as drug targets, but because these studies tend to be small and short-lived, there are many factors that can cause misjudgment. However, Hororani's team did not rely on these limited trials, they established a predictive model that combines genetic information, protein data structures, and the processes of known drugs. In the end, they obtained nearly 4,500 potential drug targets, doubling the number of previously predicted human genomes. Then, two clinicians combed out 144 drugs with the right shape and chemicals, and in addition to the target proteins that have been found to bind to them, these drugs can also bind to other proteins. Since these drugs have previously passed safety tests, this means they can be quickly used to treat other diseases. For drug developers, time is money. Researchers estimate that about 15% to 20% of new drug costs are spent on exploration. Typically, this means spending hundreds of millions of dollars and 3 to 6 years of work. Nowadays, some people hope to shorten this process to several months through AI and significantly reduce the cost of research and development. However, there is currently no drug on the market that was originally selected by the AI ​​system, but they are on the right track. One of Hingorani's collaborators is the Vice President of Benevolent AI Biomedical Informatics. BenevolentAI, a British AI company, recently signed an agreement with Janssen (a subsidiary of Johnson & Johnson) to acquire and develop clinical trial drug candidates. They plan to start the Phase IIb trial later this year. (In stage IIa, a small number of subjects will be enrolled to establish a suitable therapeutic dose; IIb is an effective group based on a to expand the sample size, to determine the effectiveness and safety of the dose.) In addition, other pharmaceutical companies are also following up. According to Lei Feng.com (public account: Lei Feng), last month, Japanese eyedrop giant Santen signed an agreement with twoXAR company in Palo Alto. Santen will use twoXAR AI technology to identify candidates for glaucoma. drug. A few weeks ago, two European companies, Pharnext and Galapagos, also announced a collaboration to develop an AI system model for new treatments for neurodegenerative diseases. However, Derek Loewe, who has long been involved in drug development research, wrote in a personal blog on Science that he is skeptical about this purely computational approach. "In the long run, I don't think this thing is impossible," he said. "But if someone tells me that they can predict the activity of all these compounds, then I might think it's nonsense. I believe that I want to see more evidence before." Companies like twoXAR are working hard to build such evidence. Last fall, they collaborated with the Asian Liver Center at Stanford University to screen 25,000 drug candidates for adult liver cancer patients. Using their own computer software, they screened 10 possible drugs in combination with genetics, proteomics, drugs and clinical data. Samuel So, director of Asian Liver Center, was very surprised by the results, because several of the drugs screened out using computer software had the same predictions as laboratory researchers, so he decided to test all 10 drug candidates. One of the most promising drugs, which kills five different liver cancer cells and does not harm healthy cells, is now ready for human trials. Currently, the only drug for the same cancer took five years to get approval from the FDA (US Food and Drug Administration), and twoXAR and Stanford have only used it for four months. What's exciting is that for industries with such high failure rates, even small advances can spur billions of dollars in markets, not to mention those that might be saved. However, unless the drugs discovered through the AI ​​system are actually on the market, there will be no fundamental changes in the R&D model of the industry. Source: Lei Feng Net API Powder is fully defined in ICH Q7A as any substance or mixture of substances intended for use in the manufacture of pharmaceutical products and, when used in pharmaceutical products, as an active ingredient of pharmaceutical products. Such substances have pharmacological activities or other direct effects in the diagnosis, treatment, symptom relief, management or prevention of disease, or can affect the function or structure of the body. The active ingredient of a medicine. Only when an API is processed into a pharmaceutical preparation can it become a medicine for clinical application. alprazolam api powder,pregabalin api powder,buy api powder,api ingredients Shaanxi YXchuang Biotechnology Co., Ltd , https://www.peptidenootropics.com
According to its source, apis are divided into chemical synthetic drugs and natural chemical drugs.
Chemical synthetic drugs can be divided into inorganic synthetic drugs and organic synthetic drugs. Inorganic synthetic drugs are inorganic compounds (very few are elements), such as aluminum hydroxide and magnesium trisilicate for the treatment of gastric and duodenal ulcer, etc. Organic synthetic drugs are mainly made of basic organic chemical raw materials, through a series of organic chemical reactions and drugs (such as aspirin, chloramphenicol, caffeine, etc.).
Natural chemical drugs can also be divided into biochemical drugs and phytochemical drugs according to their sources. Antibiotics are generally produced by microbial fermentation and belong to the category of biochemistry. In recent years, a variety of semi-synthetic antibiotics are the combination of biosynthesis and chemical synthesis products. Among apis, organic synthetic drugs account for the largest proportion of variety, yield and output value, which is the main pillar of chemical pharmaceutical industry. The quality of the API determines the quality of the preparation, so its quality standards are very strict. All countries in the world have formulated strict national pharmacopoeia standards and quality control methods for the widely used APIS.
Specific API after processing -- pharmaceutical preparations
The term API is used mainly in relation to the finished product. Mainly raw materials obtained by chemical processing means, supply raw materials for the production of finished drugs. For example, cefpirome sulfate for injection is a drug, then cefpirome sulfate is an API