Top 10 Pharma Companies of 2019 in the World & Links and YouTube Video # Vídeo @ Links about drug development & Method to convert any mouse into COVID-19 model made available to researchers – COVID-19 Antibodies Can Disappear After 2-3 Months, Study Shows – Ralph Ellis – June 19, 2020 & Open source all-atom model of entire COVID-19 S protein released @ https://en.wikipedia.org/wiki/Drug_discovery @ THE HISTORY OF VACCINES – https://www.historyofvaccines.org/timeline/all @ New vaccines: challenges of discovery – Microb Biotechnol. 2016 Sep; 9(5): 549–552. @ EDITORIAL 20 MARCH 2019 – It’s time to talk about ditching statistical significance Looking beyond a much used and abused measure would make science harder, but better. Nature 567, 283 (2019) doi: 10.1038/d41586-019-00874-8 @ WHAT DOES STATISTICALLY SIGNIFICANT MEAN? by Jeff Sauro, PhD | October 21, 2014 @ VERY IMPORTANT WEBSITES, TEXTS, LINKS AND IMAGES

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´´The world people need to have very efficient researches and projects resulting in very innovative drugs, vaccines, therapeutical substances, medical devices and other technologies according to the age, the genetics and medical records of the person. So, the treatment, diagnosis and prognosis will be very efficient and better, of course´´. Rodrigo Nunes Cal

https://science1984.wordpress.com/2021/08/14/do-the-downloads-of-very-important-detailed-and-innovative-data-of-the-world-about-my-dissertation-like-the-graphics-i-did-about-the-variations-of-weights-of-all-mice-control/

Mestrado – Dissertation – Tabelas, Figuras e Gráficos – Tables, Figures and Graphics


Impact_Fator-wise_Top100Science_Journals

GRUPO_AF1

GRUPO_AF2

GRUPO AFAN 1

GRUPO AFAN 2

Slides – mestrado

CARCINÓGENO DMBA EM MODELOS EXPERIMENTAIS

Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo

  • I DID VERY INTERESTING, INNOVATIVE, IMPORTANT AND DETAILED GRAPHICS ABOUT VARIATIONS OF ALL MICE WEIGHTS OF DIFFERENT AGES DURING ALL EXPERIMENTAL TIME OF ´´MY´´ DISSERTATION. THEY´RE AVAILABLE IN THIS BLOG AND ARE VERY IMPORTANT TO THE SCIENTIFIC COMMUNITY!! THE DIFFUSION OF RELEVANT KNOWLEDGE IS ALWAYS ESSENTIAL FOR A COUNTRY PROGRESS. NEW SCIENTIFIC DISCOVERIES NEED TO EMERGE URGENTLY !! BELOW YOU CAN DO DOWNLOAD OF THESE GRAPHICS AND OTHER DOCUMENTS RELATED TO SCIENCE, TECHNOLOGY AND INNOVATION. SO, SHARE THESE GRAPHICS AND OTHER DOCUMENTS TO OTHER PEOPLE KNOW ABOUT IT AND PERHAPS USE THEM AS AN EXCELLENT REFERENCE IN THE SCIENTIFIC RESEARCHES. @ PERSON – PEOPLE – ANALYSIS – TIME – DATA – GRAPHICS – RESEARCHES – VISION – READING – SPEAKING – LISTENING – INFORMATION – KNOWLEDGE – INTENTIONS – INNOVATIONS – CHANGES – DATA INTERPRETATIONS – NEW INNOVATIONS – INTERNET – BOOKS – GRAPHICS INTERPRETATIONS – GRAPHICS COMPARISONS – INFLUENCES – TIME – SUBSTANCES – DRUGS – VACCINES – NEW MEDICAL DEVICES – WORLD HISTORY – NEW TECHNOLOGIES – HUMAN ENERGY – WORK – NEW SCIENTIFIC DISCOVERIES – SCIENCE – GRAPHICS ANALYSIS – AGES – AGE – GENETICS – PHYSIOLOGY – MIND – MOLECULAR BIOLOGY – STATISTICS – BIOSTATISTICS – HUMAN LONGEVITY

Mestrado – ´´My´´ Dissertation – Tabelas, Figuras e Gráficos – Tables, Figures and Graphics


Impact_Fator-wise_Top100Science_Journals

GRUPO_AF1 – ´´my´´ dissertation

GRUPO_AF2 – ´´my´´ dissertation

GRUPO AFAN 1 – ´´my´´ dissertation

GRUPO AFAN 2 – ´´my´´ dissertation

Slides – mestrado – ´´my´´ dissertation

CARCINÓGENO DMBA EM MODELOS EXPERIMENTAIS

Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo – ´´my´´ dissertation

Positive Feedbacks by Facebook and Twitter about this Blog, like the very important, innovative and detailed graphics I did about variations of all mice weights (Control and Study Groups) of different ages during all experimental time of ´´my´´ dissertation. Note: I have received positive feedbacks about this Blog by LinkedIn, E-mails and Instagram too. @ Internet invitations I received by direct messages to participate in very important science events worldwide in less than 1 year because I participated of great researches in Brazil and other informations @ Links & The next step in nanotechnology | George Tulevski & Animated Nanomedicine movie @ Nanotechnology Animation & Powering Nanorobots & The World’s Smallest Robots: Rise of the Nanomachines & Building Medical Robots, Bacteria sized: Bradley Nelson at TEDxZurich @ Mind-controlled Machines: Jose del R. Millan at TEDxZurich & The present and future of brain-computer interfaces: Avi Goldberg at TEDxAsheville & Future of human/computer interface: Paul McAvinney at TEDxGreenville 2014 @ Bio-interfaced nanoengineering: human-machine interfaces | Hong Yeo | TEDxVCU @ Very important images, websites, social networks and links – https://science1984.wordpress.com/2019/03/17/feedbacks-on-facebook-related-to-researches-i-participated-in-brazil-for-example-the-graphics-i-did-about-variations-of-all-mice-weights-control-and-study-groups-of-different-ages-during-all-exper/

CARCINÓGENO DMBA EM MODELOS EXPERIMENTAIS

monografia – ´´my´´ monograph

Feedback positivo de pessoas sobre minha dissertação pelo Messenger – Facebook. Positive feedback of people about my dissertation, blog and YouTube channel by Facebook – Messenger. Ano – Year: 2018

My suggestion of a very important Project…

rodrigonunescal_dissertation

Apostila – Pubmed

LISTA DE NOMES – PEOPLE´S NAMES – E-MAIL LIST – LISTA DE E-MAILS

A Psicossomática Psicanalítica

O Homem como Sujeito da Realidade da Saúde – Redação

ÁCIDO HIALURONICO

As credenciais da ciência (1)

Aula_Resultados – Results

Frases que digitei – Phrases I typed

Nanomedicina – Texto que escrevi. Nanomedicine – Text I typed(1)

Nanomedicine123(2)57

Genes e Epilepsia

MÉTODOS DE DOSAGEM DO ÁCIDO HIALURÔNICO

microbiologia-famerp – Copia

Impact_Fator-wise_Top100Sciene_Journals

Positive feedback of people about my dissertation, blog and YouTube channel by Messenger (Facebook). Feedback positivo de pessoas sobre minha dissertação, blog e canal do YouTube pelo Facebook (Messenger) Year / Ano: 2018 – positive-feedback-of-people-about-my-dissertation-blog-and-youtube-channel-by-facebook-messenger-ano-year-2018

https://www.nature.com/articles/d41586-019-00874-8

What Does Statistically Significant Mean?

Top 10 Pharma Companies of 2019 in the World – Links and YouTube Video – Vídeo @ Industries – Companies – Schools – Faculties – EXCELENTES IDEIAS – Laboratories – EXCELLENT IDEAS – Universities – K N O W L E D G E M E N T – MELHORIA(S) – DIFFUSION -INFORMATION – G R A P H I C = GRÁFICO — INFORMAÇÕES – GRAPHICS –- G R Á F I C O (S) – – Analysis of internal and external factors – READING – VISION – LEITURA(S) – VISÃO – MICE AND HUMAN RESEARCHES – PESQUISAS EM CAMUNDONGOS E EM HUMANOS – PROBABILITIES – PROBABILIDADE(S) – HUMAN RESEARCHES – ANIMAL TESTING –- – B I R T H – NASCIMENTO(S) — A G E S – I D A D E (S) – I D A D E – A G E – TEMPO — TIME – WEIGHTS – Mice Research – FUTURE REFERENCES – FUTURE REFERENCE — R E S E A R C H E S – DETAILS – Physiological and Biochemical Interactions – I M P R O V E M E N T – Impact Factors – Citations – Journals – Papers – BOOKS – LIVRO(S) – VERY GOOD INNOVATIONS – I M P O R T A N C E – INOVAÇÃO(ÕES) MUITO BOA(S) – RESULTS – RESULTADO(S) – Statistics – Countries – Cities – World – Name – NOME(S) – DISEASES – NAMES – FUTURE – PAST – PRESENT – Photo – FOTO(S) – Website – Ranking – SITE(S) – MONEY – DINHEIRO – WORK – P E O P L E – PERSON – LIVES – AGES – IDADE(S) – VIDA(S) – HUMAN EXPECTANCY OF LIFE – ENERGIA(S) – LIFE – ENERGY –(E) – Life Experiences – MEMORIES – DISEASES – HEALTH – HISTORY – PRIZES – REFLECTIONS – CORRECT INTERPRETATIONS – THINKING – THOUGHTS – FEELINGS – MINDS – MIND – BRILLIANT IDEAS – EXCELLENT PROJECTS – EFFICIENT RESEARCHES – HUMAN RELATIONS – E – M A I L S @ SCIENTIFIC DISCOVERY – VACCINES – SUBSTANCES – TESTS – 100 – SUBSTÂNCIA(S) – TESTE(S) – SEMELHANÇA(S) GENÉTICA(S) – GENETIC SIMILARITIES – GENE – GENES – GENE(S) – MEDICAMENTO(S) – EXEMPLO(S) – EXAMPLES – ASSUNTO(S) – SUBJECTS – COMPUTER LANGUAGE – SEM – CEM – 100 – LINGUAGENS DE PROGRAMAÇÃO – MIND – MENTE – INTENTIONS – INTENÇÕES – SIGNIFICADO(S) – MEANS – SUBJECT@1/01 100ZERO 0 (E)x (e)+/-= E – 01I I 2 V -I 01 1 5 V X I I 1 2 I V X 14 15 5 VII 2 7 VIII 8 5 V I 106 0X X 20 10 XX– O o , 0 O 1 — 1 0 >< 3 I EU 1 E U 10X 100X 0X III I I 2I 01 10 — 1 0 , 0 – 0 1 , 10, 0¨(01 , 1 ; 0 1 I , Tamanho(s)size0_Espaço Vogals Vogais EXEMPLO(S) / EXAMPLES : NO JOGO (S) J O G O(S) 100TE SENTE E (E) NO GAME NO 100E XX 20 02 II 0 2 2 VC VC @ &(5 V¨_,. 😉 , 01I 0 1 I EU i t te_ VÊ SE VOCÊ V C VC 100 VC (VOCÊ) 100 VOCÊ YOU SEM VOCÊ(S) VOCÊS — Consoante(s) :_&*ªt?Ii1-EU/USA+,-l!%/º+§$#´´~^ 1X2 1x 2} ]y20XX10x;10,W W W NO GAME NO GAMES (S) N O @_ 100_ Espaço 0 1 0 0, 100_ 0zero 01x 100,0-x; 100PRE 100ESPAÇO_ E S P A C E _01 , 1 0 0 S E M P R E S E N T E , 100TE SEM TE E 100 NUMBER 0 1 100 ; 1_0 0 SIZE 0,0001 TAMANHO(S) NUMBERS NÚMERO_(S) 10 X LETTERS LETRA(S) WORDS PALAVRA(S) www _,.;:_SIZES/_,.;TAMANHO(S) ;():} (S)(_?) 2×3 2X3 2 X (10) 3 6 ; 0 X 0 , 0 10 0 / SEMPRE SEM 100 CEM * 1 , 01 , I).,0 espaço0 e;vE:.,(S)_..´´(5)V. 100_ SEM LIMITE(S) NO LIMITE(S) NO LIMITS LIMIT – SEM (100) – READING – LEITURA #_ SPEAK @– FALA # TYPING – DIGITAR # ESCREVER – WRITE — NO(S) TIME(S) – SEM TIME(S) – CEM / SEM / 100 (100) TEMPO – – N O TIME/ENERGY@ DRUGS – DECIFRATION – FORMULAS – FÓRMULA(S) – EFFICIENCY – EFICIÊNCIA – E N E R G Y – D N A – RNAr – C E L L –– MORTE(S) — DEATH — IDADE(S) – IDADE — A G E — P E R S O N — P E O P L E — PESSOAS – – PESSOA(S) — D I S E A S E S (?time) — NAMES (Time?) – — AGES – – – D O E N Ç A (S) – — NOME(S) — CÉLULA(S) — DEATHS – MORTE — – SCIENTIFIC COMMUNITY – VELOCIDADE(S) – VELOCITY – INTERNET – COMUNIDADE(S) CIENTÍFICA(S) – WAYS OF COMMUNICATIONS – FORMA(S) DE COMUNICAÇÃO(ÕES) – SOCIETY – SOCIEDADE(S) – INTERNET SOCIETY – ANTIQUITY – ANCESTORS – ANTIGUIDADE – SYMBOLS – SÍMBOLO(S) – CODES – CÓDIGO(S) – GENERATIONS – GERAÇÕES – MESSAGES – MENSAGENS – BIOINFORMATICS – DIFFUSION OF KNOWLEDGEMENT – DIFUSÃO DE CONHECIMENTOS – HISTORY OF SCIENCE IN THE WORLD – GENETIC ENGINEERING – BIOTECHNOLOGY @ ´´Drug development is the process of bringing a new pharmaceutical drug to the market once a lead compound has been identified through the process of drug discovery. It includes preclinical research on microorganisms and animals, filing for regulatory status, such as via the United States Food and Drug Administration for an investigational new drug to initiate clinical trials on humans, and may include the step of obtaining regulatory approval with a new drug application to market the drug.[1][2]´´ @ Data – DADOS – Very Important LINKS – IMAGES – Anatomy – PHARMACOLOGY – Pathology – G E N E T I C S – DNA – HUMAN AGE – Epidemiology – RNAm – BIOCHEMISTRY – Molecular Biology – BIOLOGIA MOLECULAR – BIOINFORMATICS – BIOINFORMÁTICA – HUMAN AND EXPERIMENTAL PATHOLOGY – PATOLOGIA HUMANA E EXPERIMENTAL – HUMAN AND EXPERIMENTAL PHYSIOLOGY – FISIOLOGIA HUMANA E EXPERIMENTAL – Biostatistics – GENÉTICA – Statistics – ESTATÍSTICA – POSTERS -@ – POSTER(S) – GRÁFICO(S) DA(S) PESQUISA(S) – RESEARCH GRAPHICS –@ REFERENCES – REFERÊNCIA(S) &– PHYSIOLOGICAL SUBSTANCES – (S) – VISIBILITY – VISIBILIDADE — SCIENTIFIC DISCOVERIES – – DESCOBERTA(S) CIENTÍFICA(S) &– LONGEVIDADE — Researches – Articles – R E F E R E N C E S — Journals – Papers – Science Magazines — SCIENTIFIC DISCOVERIES – SCIENCE MAGAZINES – IMPACT FACTORS – FATOR(ES) DE IMPACTO(S) –& LONGEVITY – Gene(s) – NO GENE – (S) – NO(S) GENE(S) – 100 GENES – SEM GENE – 0 GENE – SEM – 100 – GENES – 100 – GENE – @ VERY LITTLE DETAILS @- PEQUENO(S) DETALHE(S) – GENETIC MUTATIONS – Mutações Genéticas – Proteína(s) – P r o t e i n s – EPIDEMIOLOGIA – JORNAIS CIENTÍFICOS – PESQUISAR – ANALISAR – DETAILED ANALYSIS – EQUIPE(S) – TEAMS – DADO(S) VERDADEIRO(S) – TRUE DATA – SCIENTIFIC INNOVATIONS – INOVAÇÃO(ÕES) CIENTÍFICA(S) – REVISTA(S) CIENTÍFICA(S) – ANÁLISE(S) MINUCIOSA(S) – PESQUISA(S) — INVESTIGATIONS – INVESTIGAÇÕES – PESQUISA(S) CIENTÍFICA(S) – ARTIGO(S) CIENTÍFICO(S) – PERIÓDICO(S) CIENTÍFICO(S) – WORLD HISTORY – HISTÓRIA MUNDIAL & https://en.wikipedia.org /wiki/Drug_development @ https://www.fda.gov/patients/learn-about-drug-and-device-approvals/drug-development-process # & @ https://en.wikipedia.org/wiki/COVID-19_drug_development @´´Sundar Pichai was appointed CEO of Google, replacing Larry Page who became the CEO of Alphabet. The company’s rapid growth since incorporation has triggered a chain of products, acquisitions, and partnerships beyond Google’s core search engine (Google Search).´´ https://en.wikipedia.org/wiki/Interpretability @ Method to convert any mouse into COVID-19 model made available to researchers – COVID-19 Antibodies Can Disappear After 2-3 Months, Study Shows Ralph Ellis June 19, 2020 – Open source all-atom model of entire COVID-19 S protein released Researchers have developed a video and model-building programme for other scientists to build full-length COVID-19 S protein models. @ https://en.wikipedia.org/wiki/Drug_discovery @ THE HISTORY OF VACCINES – https://www.historyofvaccines.org/timeline/all @ New vaccines: challenges of discovery – Microb Biotechnol. 2016 Sep; 9(5): 549–552. @ VERY IMPORTANT WEBSITES, TEXTS, LINKS AND IMAGES

https://en.wikipedia.org/wiki/Drug_discovery

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4993171/

https://www.historyofvaccines.org/timeline/all

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4993171/

WWW.GOOGLE.COM WWW.GMAIL.COM WWW.YAHOO.COM WWW.FACEBOOK.COM WWW.FACEBOOK.COM/SCIENTIFICBLOG WWW.NASA.GOV WWW.NOBELPRIZE.ORG WWW.LINKEDIN.COM WWW.YOUTUBE.COM WWW.WORDPRESS.COM WWW.SCIENCE1984.WORDPRESS.COM WWW.WIKIPEDIA.ORG WWW.STANFORD.EDU WWW.CALTECH.EDU WWW.FAMERP.BR WWW.ITA.BR WWW.FORBES.COM

Top 10 Pharma Companies of 2019

https://en.wikipedia.org /wiki/Drug_development

https://en.wikipedia.org/wiki/COVID-19_drug_development

Method to convert any mouse into COVID-19 model made available to researchers

https://www.proclinical.com/blogs/2019-3/the-top-10-pharmaceutical-companies-in-the-world-2019

https://www.fiercepharma.com/special-report/top-20-pharma-companies-by-2019-revenue

https://www.fda.gov/patients/learn-about-drug-and-device-approvals/drug-development-process

Open source all-atom model of entire COVID-19 S protein released

 
 
 
 
 
Horizon
 
 
 
 
NEWS

Method to convert any mouse into COVID-19 model made available to researchers

Researchers who reported that delivering the human ACE2 protein into mouse airway cells creates COVID-19 models have released their findings to allow other scientists to make their own models.

Mouse model of COVID-19

Researchers have created a gene therapy approach that they say can convert any lab mouse into one that can be infected with SARS-CoV-2 and develop COVID-like lung disease. The team have made their gene therapy vector freely available to any researchers who wish to use it. The study was conducted by scientists at the University of Iowa (UI) Carver College of Medicine, US, and Medical University, Guangzhou, China.

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“There is a pressing need to understand this disease and to develop preventions and treatments,” said Dr Paul McCray, UI professor of paediatrics and microbiology and immunology. “We wanted to make it as easy as possible for other researchers to have access to this technology, which allows any lab to be able to immediately start working in this area by using this trick.”

According to the researchers, the ‘trick’ is the use of an adenovirus gene therapy vector that is inhaled by the mice to deliver the human angiotensin-converting enzyme 2 (ACE2) protein into mouse airway cells. This is the protein that SARS-CoV-2 uses to infect cells. Once the mouse airway cells express the hACE2 protein, the mice become susceptible to infection with the virus and they develop COVID-19-like lung symptoms.

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Due to differences between the human and mouse ACE2 protein, wild-type mice are not susceptible to the SARS-Cov-2 virus, highlighting the need to develop models. Although the disease is not fatal in the models, the animals do get sick, losing weight and developing lung damage.

The vector is readily adaptable to any strain of mice (and other lab animals), which means research teams can rapidly convert mice with specific genetic traits into animals that are susceptible to SARS-Cov-2, allowing them to test whether those traits influence the disease.

The researchers showed that mice treated with this gene therapy could be used to evaluate a vaccine and several potential COVID-19 therapies, including a preventative strategy known as poly I:C, which boosts the innate immune response, convalescent plasma from recovered COVID-19 patients and the antiviral drug remdesivir. In each case, the therapies prevented weight loss, reduced lung disease and increased the speed of virus clearance in the mice. The team also showed that the mice are useful for studying important immune responses involved in clearing the SARS-CoV-2 virus.

The team say that their gene therapy vector is essentially an off-the-shelf tool that allows labs to create their own COVID-19 mouse model within a few days.

“You can create these mice very quickly. You do not have to breed the strain, which is very time consuming and expensive,” McCray explained. “We think this technology will be useful for investigating COVID-19 lung disease and rapidly testing interventions that people think are promising for treating or preventing COVID-19.”

The results were published in Cell

 
 
Horizon
 
 
 
 
NEWS

Open source all-atom model of entire COVID-19 S protein released

Researchers have developed a video and model-building programme for other scientists to build full-length COVID-19 S protein models.

COVID-19 S protein

An international group of researchers has released the first open-source all-atom models of a full-length Spike (S) protein, which the SARS-CoV-2 virus uses to enter human cells and cause COVID-19. The researchers say this protein is of particular importance as a vaccine and antiviral drug development target.

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The team was made up of researchers from Seoul National University in South Korea, University of Cambridge in the UK and Lehigh University in the US.

The scientists have created a video to illustrate how to build this membrane system from their SARS-CoV-2 S protein models. The model-building programme is open access and can be found here.

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In our one-hour session on the 23 June at 16:00 BST, you will see how Vertex Pharmaceuticals and AstraZeneca are exploring the power of CETSA®-HT, in conjunction with Alpha technology for detection, for larger scale primary HTS campaigns in close collaboration with Pelago BioScience and PerkinElmer.

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The graphical user interface the researchers used is a programme that simulates complex biomolecular systems simply, precisely and quickly. It enables scientists to understand molecular-level interactions that cannot be observed any other way.

The S protein structure was determined using cryo-EM with the receptor binding domain (RBD) up and with the RBD down. However, the researchers say this model had many missing residues. So, they first modelled the missing amino acid residues and then other missing domains. In addition, they modelled all potential glycans attached to the S protein. These glycans prevent antibody recognition, which makes it difficult to develop a vaccine. They also built a viral membrane system of an S protein for molecular dynamics simulation.

S protein

“Our models are the first fully-glycosylated full-length SARS-CoV-2 S protein models that are available to other scientists,” said Professor Wonpil Im, at Lehigh University’s Department of Biological Sciences and Bioengineering Department. “Our team spent days and nights to build these models very carefully from the known cryo-electron microscopy (cryo-EM) structure portions. Modelling was very challenging because there were many regions where simple modelling failed to provide high-quality models.”

According to the researchers, scientists can use the models to conduct innovative and novel simulation research for the prevention and treatment of COVID-19.

The team recommends reading the this preprint paper before using any of the models. The results of the new study are published in The Journal of Physical Chemistry B

 
 
 
 
 
 

Drug development

From Wikipedia, the free encyclopedia
 
 

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Drug discovery cycle schematic
 

Drug development is the process of bringing a new pharmaceutical drug to the market once a lead compound has been identified through the process of drug discovery. It includes preclinical research on microorganisms and animals, filing for regulatory status, such as via the United States Food and Drug Administration for an investigational new drug to initiate clinical trials on humans, and may include the step of obtaining regulatory approval with a new drug application to market the drug.[1][2]

New chemical entity development[edit]

Broadly, the process of drug development can be divided into preclinical and clinical work.

 

Timeline showing the various drug approval tracks and research phases[3]

Pre-clinical[edit]

New chemical entities (NCEs, also known as new molecular entities or NMEs) are compounds that emerge from the process of drug discovery. These have promising activity against a particular biological target that is important in disease. However, little is known about the safety, toxicitypharmacokinetics, and metabolism of this NCE in humans. It is the function of drug development to assess all of these parameters prior to human clinical trials. A further major objective of drug development is to recommend the dose and schedule for the first use in a human clinical trial (“first-in-human” [FIH] or First Human Dose [FHD], previously also known as “first-in-man” [FIM]).

In addition, drug development must establish the physicochemical properties of the NCE: its chemical makeup, stability, and solubility. Manufacturers must optimize the process they use to make the chemical so they can scale up from a medicinal chemist producing milligrams, to manufacturing on the kilogram and ton scale. They further examine the product for suitability to package as capsulestablets, aerosol, intramuscular injectable, subcutaneous injectable, or intravenous formulations. Together, these processes are known in preclinical and clinical development as chemistry, manufacturing, and control (CMC).

Many aspects of drug development focus on satisfying the regulatory requirements of drug licensing authorities. These generally constitute a number of tests designed to determine the major toxicities of a novel compound prior to first use in humans. It is a legal requirement that an assessment of major organ toxicity be performed (effects on the heart and lungs, brain, kidney, liver and digestive system), as well as effects on other parts of the body that might be affected by the drug (e.g., the skin if the new drug is to be delivered through the skin). Increasingly, these tests are made using in vitro methods (e.g., with isolated cells), but many tests can only be made by using experimental animals to demonstrate the complex interplay of metabolism and drug exposure on toxicity.

The information is gathered from this preclinical testing, as well as information on CMC, and submitted to regulatory authorities (in the US, to the FDA), as an Investigational New Drug (IND) application. If the IND is approved, development moves to the clinical phase.

Clinical phase[edit]

Clinical trials involve three or four steps:[4]

  • Phase I trials, usually in healthy volunteers, determine safety and dosing.
  • Phase II trials are used to get an initial reading of efficacy and further explore safety in small numbers of patients having the disease targeted by the NCE.
  • Phase III trials are large, pivotal trials to determine safety and efficacy in sufficiently large numbers of patients with the targeted disease. If safety and efficacy are adequately proved, clinical testing may stop at this step and the NCE advances to the new drug application (NDA) stage.
  • Phase IV trials are post-approval trials that are sometimes a condition attached by the FDA, also called post-market surveillance studies.

The process of defining characteristics of the drug does not stop once an NCE begins human clinical trials. In addition to the tests required to move a novel drug into the clinic for the first time, manufacturers must ensure that any long-term or chronic toxicities are well-defined, including effects on systems not previously monitored (fertility, reproduction, immune system, among others). They must also test the compound for its potential to cause cancer (carcinogenicity testing).

If a compound emerges from these tests with an acceptable toxicity and safety profile, and the company can further show it has the desired effect in clinical trials, then the NCE portfolio of evidence can be submitted for marketing approval in the various countries where the manufacturer plans to sell it. In the United States, this process is called a “new drug application” or NDA.

Most NCEs fail during drug development, either because they have unacceptable toxicity or because they simply do not have the intended effect on the targeted disease as shown in clinical trials.

A trend toward the collection of biomarker and genetic information from clinical trial participants, and increasing investment by companies in this area, led by 2018 to fully half of all drug trials collecting this information, the prevalence reaching above 80% among oncology trials.[5]

Cost[edit]

One 2010 study assessed both capitalized and out-of-pocket costs for bringing a single new drug to market as about US$1.8 billion and $870 million, respectively.[6] A median cost estimate of 2015-16 trials for development of 10 anti-cancer drugs was US$648 million.[7] In 2017, the median cost of a pivotal trial across all clinical indications was $19 million.[8] The average cost for a pivotal trial to demonstrate its equivalence or superiority to an existing approved drug was $347 million.[8]

The full cost of bringing a new drug (i.e., new chemical entity) to market – from discovery through clinical trials to approval – is complex and controversial. Typically, companies spend tens to hundreds of millions of U.S. dollars.[8][9] One element of the complexity is that the much-publicized final numbers often not only include the out-of-pocket expenses for conducting a series of Phase I-III clinical trials, but also the capital costs of the long period (10 or more years) during which the company must cover out-of-pocket costs for preclinical drug discovery.

In an analysis of the drug development costs for 98 companies over a decade, the average cost per drug developed and approved by a single-drug company was $350 million.[10] But for companies that approved between eight and 13 drugs over 10 years, the cost per drug went as high as $5.5 billion, due mainly to geographic expansion for marketing and ongoing costs for Phase IV trials and continuous monitoring for safety.[10]

Alternatives to conventional drug development have the objective for universities, governments, and the pharmaceutical industry to collaborate and optimize resources.[11]

Valuation[edit]

The nature of a drug development project is characterised by high attrition rates, large capital expenditures, and long timelines. This makes the valuation of such projects and companies a challenging task. Not all valuation methods can cope with these particularities. The most commonly used valuation methods are risk-adjusted net present value (rNPV), decision treesreal options, or comparables.

The most important value drivers are the cost of capital or discount rate that is used, phase attributes such as duration, success rates, and costs, and the forecasted sales, including cost of goods and marketing and sales expenses. Less objective aspects like quality of the management or novelty of the technology should be reflected in the cash flows estimation.[12][13]

Success rate[edit]

Candidates for a new drug to treat a disease might, theoretically, include from 5,000 to 10,000 chemical compounds. On average about 250 of these show sufficient promise for further evaluation using laboratory tests, mice and other test animals. Typically, about ten of these qualify for tests on humans.[14] A study conducted by the Tufts Center for the Study of Drug Development covering the 1980s and 1990s found that only 21.5 percent of drugs that started Phase I trials were eventually approved for marketing.[15] In the time period of 2006 to 2015, the success rate was 9.6%.[16] The high failure rates associated with pharmaceutical development are referred to as the “attrition rate” problem. Careful decision making during drug development is essential to avoid costly failures.[17] In many cases, intelligent programme and clinical trial design can prevent false negative results. Well-designed, dose-finding studies and comparisons against both a placebo and a gold-standard treatment arm play a major role in achieving reliable data.[18]

Novel initiatives to boost development[edit]

Novel initiatives include partnering between governmental organizations and industry, such as the European Innovative Medicines Initiative.[19] The US Food and Drug Administration created the “Critical Path Initiative” to enhance innovation of drug development,[20] and the Breakthrough Therapy designation to expedite development and regulatory review of candidate drugs for which preliminary clinical evidence shows the drug candidate may substantially improve therapy for a serious disorder.[21]

See also[edit]

References[edit]

  1. ^ Strovel, Jeffrey; Sittampalam, Sitta; Coussens, Nathan P.; Hughes, Michael; Inglese, James; Kurtz, Andrew; Andalibi, Ali; Patton, Lavonne; Austin, Chris; Baltezor, Michael; Beckloff, Michael; Weingarten, Michael; Weir, Scott (July 1, 2016). “Early Drug Discovery and Development Guidelines: For Academic Researchers, Collaborators, and Start-up Companies”Assay Guidance Manual. Eli Lilly & Company and the National Center for Advancing Translational Sciences.
  2. ^ Taylor, David (2015). “The Pharmaceutical Industry and the Future of Drug Development”Issues in Environmental Science and Technology. Royal Society of Chemistry: 1–33. doi:10.1039/9781782622345-00001ISBN 978-1-78262-189-8.
  3. ^ Kessler, David A.; Feiden, Karyn L. (1995). “Faster Evaluation of Vital Drugs”. Scientific American272 (3): 48–54. Bibcode:1995SciAm.272c..48Kdoi:10.1038/scientificamerican0395-48PMID 7871409.
  4. ^ Ciociola AA; et al. (May 2014). “How drugs are developed and approved by the FDA: current process and future directions”. Am J Gastroenterol109 (5): 620–3. doi:10.1038/ajg.2013.407PMID 24796999.
  5. ^ Miseta, Ed (August 17, 2018). “Gene Therapies Create Moral Dilemma For Clinical Researchers”Clinical Leader. Pennsylvania, United States: VertMarkets, Inc.
  6. ^ Paul, Steven M.; Mytelka, Daniel S.; Dunwiddie, Christopher T.; Persinger, Charles C.; Munos, Bernard H.; Lindborg, Stacy R.; Schacht, Aaron L. (2010). “How to improve R&D productivity: The pharmaceutical industry’s grand challenge”. Nature Reviews Drug Discovery9 (3): 203–14. doi:10.1038/nrd3078PMID 20168317.
  7. ^ Prasad, Vinay; Mailankody, Sham (1 October 2017). “Research and development spending to bring a single cancer drug to market and revenues after approval”JAMA Internal Medicine177 (11): 1569. doi:10.1001/jamainternmed.2017.3601ISSN 2168-6106PMC 5710275PMID 28892524.
  8. Jump up to:a b c Moore, Thomas J.; Zhang, Hanzhe; Anderson, Gerard; Alexander, G. Caleb (1 October 2018). “Estimated costs of pivotal trials for novel therapeutic agents approved by the US Food and Drug Administration, 2015-2016”JAMA Internal Medicine178 (11): 1451. doi:10.1001/jamainternmed.2018.3931ISSN 2168-6106PMC 6248200PMID 30264133.
  9. ^ Sertkaya, A; Wong, H. H.; Jessup, A; Beleche, T (2016). “Key cost drivers of pharmaceutical clinical trials in the United States”. Clinical Trials13 (2): 117–26. doi:10.1177/1740774515625964PMID 26908540.
  10. Jump up to:a b Herper, Matthew (11 August 2013). “The Cost Of Creating A New Drug Now $5 Billion, Pushing Big Pharma To Change”. Forbes, Pharma & Healthcare. Retrieved 17 July 2016.
  11. ^ Maxmen A (2016). “Busting the billion-dollar myth: how to slash the cost of drug development”. Nature536 (7617): 388–90. Bibcode:2016Natur.536..388Mdoi:10.1038/536388aPMID 27558048.
  12. ^ Boris Bogdan and Ralph Villiger, “Valuation in Life Sciences. A Practical Guide”, 2008, 2nd edition, Springer Verlag.
  13. ^ Nielsen, Nicolaj Hoejer “Financial valuation methods for biotechnology”, 2010. “Archived copy” (PDF). Archived from the original (PDF) on 2012-03-05. Retrieved 2014-11-25.
  14. ^ Stratmann, Dr. H.G. (September 2010). “Bad Medicine: When Medical Research Goes Wrong”. Analog Science Fiction and FactCXXX (9): 20.
  15. ^ “R&D costs are on the rise”Medical Marketing and Media38 (6): 14. June 1, 2003. Archived from the original on October 18, 2016.
  16. ^ “Clinical Development Success Rates 2006-2015” (PDF)BIO Industry Analysis. June 2016.
  17. ^ Wang Y. (2012). “Extracting Knowledge from Failed Development Programmes”Pharm Med26 (2): 91–96. doi:10.1007/BF03256897.
  18. ^ Herschel, M. (2012). “Portfolio Decisions in Early Development: Don’t Throw Out the Baby with the Bathwater”Pharm Med26 (2): 77–84. doi:10.1007/BF03256895. Archived from the original on 2012-06-16. Retrieved 2012-06-12.
  19. ^ “About the Innovative Medicines Initiative”. European Innovative Medicines Initiative. 2020. Retrieved 24 January 2020.
  20. ^ “Critical Path Initiative”. US Food and Drug Administration. 23 April 2018. Retrieved 24 January 2020.
  21. ^ “Breakthrough Therapy”. US Food and Drug Administration. 4 January 2018. Retrieved 24 January 2020.

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