scholarly journals Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Nagasundaram Nagarajan ◽  
Edward K. Y. Yapp ◽  
Nguyen Quoc Khanh Le ◽  
Balu Kamaraj ◽  
Abeer Mohammed Al-Subaie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Computational Biology ◽  
New Drugs ◽  
Pharmaceutical Companies ◽  
Data Sets ◽  
Aggregated Data ◽  
Usable Knowledge ◽  
Knowledge Of Cancer ◽  
Generation Sequencing

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into “usable” knowledge. Being well aware of this, the world’s leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.

scholarly journals Drug Discovery Firms and Business Alliances for Sustainable Innovation

2021 ◽  
Vol 13 (7) ◽  
pp. 3599
Author(s):  
Yoshimi Harada ◽  
Huayi Wang ◽  
Kota Kodama ◽  
Shintaro Sengoku
Keyword(s):  
Drug Discovery ◽  
Initial Public Offering ◽  
New Drugs ◽  
Pharmaceutical Companies ◽  
Pharmaceutical Firms ◽  
Significant Research ◽  
Public Offering ◽  
Platform Leadership ◽  
Available Information ◽  
Startup Firms

Biotech startup firms developing pharmaceutical seeds from scientific and technological innovation are burdened by significant Research & Development (R&D) expenses, long-term R&D operations, and low probability of R&D success. To address these challenges while sustainably creating innovations and new drugs, business alliances with existing pharmaceutical companies are one of the most important issues on the management agenda. The present study explores the necessity and significance of business alliances with pharmaceutical companies for the development of drug-discovery by Japanese biotech startup firms under high uncertainty. This study investigates the types of alliances to understand the origins of sustainability of these creative activities. First, we investigate and analyze the details of the partnership and its impact on the products under development based on the publicly available information of 16 drug discovery biotech startup firms in Japan that had become public since 2010. As a result, all firms continued their operations with the funds obtained from the business alliances with pharmaceutical firms at the time of their initial public offering (IPO). In addition, 56% of these firms’ alliance projects (n = 73) were seeded-out, and 32% seeded-in, indicating that they had adopted flexible alliance strategies not limited to seed-out ones. For sustainable going concern of the biotech startup business, it is valuable to consider multiple strategic options: “in-licensing and value up”, “best-in-class”, “platform leadership” and “first-in-class” depending on the characteristics of seeds and environmental restrictions.

Smart, Innovative and Intelligent Technologies Used in Drug Designing

2014 ◽  
pp. 285-301
Author(s):  
S. Deshpande ◽  
S. K. Basu ◽  
X. Li ◽  
X. Chen
Keyword(s):  
Drug Discovery ◽  
Protein Structures ◽  
Three Dimensional ◽  
New Drugs ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Mathematical Methods ◽  
Structure Based Drug Design ◽  
Recent Developments ◽  
Therapeutic Compounds

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

scholarly journals Smart, Innovative and Intelligent Technologies Used in Drug Designing

2017 ◽  
pp. 1175-1191
Author(s):  
S. Deshpande ◽  
S. K. Basu ◽  
X. Li ◽  
X. Chen
Keyword(s):  
Drug Discovery ◽  
Protein Structures ◽  
Three Dimensional ◽  
New Drugs ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Mathematical Methods ◽  
Structure Based Drug Design ◽  
Recent Developments ◽  
Therapeutic Compounds

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

Blockbuster Drugs

2014 ◽  
Author(s):  
Jie Jack Li
Keyword(s):  
Drug Discovery ◽  
Pharmaceutical Industry ◽  
Prescription Drugs ◽  
Business Models ◽  
Scientific Discovery ◽  
New Drugs ◽  
Pharmaceutical Companies ◽  
Blockbuster Drug ◽  
A Minor ◽  
Future Landscape

For the world's largest prescription drug manufacturers, the last few years have been a harrowing time. Recently, Pfizer's Lipitor, GlaxoSmithKline's Advair, AstraZeneca's Seroquel, and Sanofi-Aventis and Bristol-Myers Squibb's Plavix all came off patent in the crucial U.S. market. This so-called "patent cliff" meant hundreds of billions of dollars in lost revenue and has pharmaceutical developers scrambling to create new drugs and litigating to extend current patent protections. Having spent most of his career in drug discovery in "big pharma," Dr. Li now delivers an insider's account of how the drug industry ascended to its plateau and explores the nature of the turmoil it faces in the coming years. He begins with a survey of the landscape before "blockbuster drugs," and proceeds to describe how those drugs were discovered and subsequently became integral to the business models of large pharmaceutical companies. For example, in early 1980s, Tagamet, the first "blockbuster drug," transformed a minor Philadelphia-based drug maker named SmithKline & French into the world's ninth-largest pharmaceutical company in terms of sales. The project that delivered Tagamet was nearly terminated several times because research efforts begun in 1964 produced no apparent results within the first eleven years. Similar stories accompany the discovery and development of now-ubiquitous prescription drugs, among them Claritin, Prilosec, Nexium, Plavix, and Ambien. These stories, and the facets of the pharmaceutical industry that they reveal, can teach us valuable lessons and reveal many crucial aspects about the future landscape of drug discovery. As always, Dr. Li writes in a readable style and intersperses fascinating stories of scientific discovery with engaging human drama.

scholarly journals Complexity Theory: Artificial Intelligence System Help Safety Improvement in the Next Pandemic

2020 ◽  
Vol 5 (09) ◽  
pp. 362-368
Author(s):  
Shahad Faisal Halabi
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Complexity Theory ◽  
High Performance ◽  
New Drugs ◽  
Hospital Environment ◽  
Western World ◽  
Intelligence System ◽  
Safety Improvement ◽  
The Impact

As the coronavirus pandemic spread from Asia to the western world, drug discovery came to a near standstill. Most laboratories shut down and instruments and reagents were left untouched, except for the most essential work. The pandemic forced large and small companies, regulatory and government agencies, and academia to tap into technology, particularly artificial intelligence (AI) and machine learning (ML), for providing more than just speed and efficiency. This essay aims to dig deeply in complexity theory to help improve safety and reduce the impact of the next pandemic. It is based on implementing Artificial Intelligence (AI) to provide the safer complex theory with an example of the current situation of COVID-19. While there are no shortcuts around scientific rigor and experimentation, AI can certainly accelerate the discovery of new drugs particularly when combined with high-performance computing (HPC) and quantum computing. Evaluating new AI technologies, particularly in areas of drug discovery where there are few demonstrations of success, can be a real challenge. It is considered that safety improvement of alert systems and the risk factors, in order to organize the safety of health facilities and control the hospital environment before the potential pandemic develops. Here, we will try to apply complexity theory in our dealing with future pandemics based on the situation analysis of previous experiences.

Clinical characteristics, co-mutations and outcomes of advanced non-small cell lung cancer (NSCLC) patients with KRAS mutations.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21174-e21174
Author(s):  
Arun Muthiah ◽  
Rani Chudasama ◽  
Adam J. Olszewski ◽  
Harish Saiganesh ◽  
Habibe Kurt ◽  
...  
Keyword(s):  
Advanced Nsclc ◽  
Initial Therapy ◽  
New Drugs ◽  
Molecular Characteristics ◽  
Data Sets ◽  
Kras Gene ◽  
Kras Mutations ◽  
Nsclc Patients ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

e21174 Background: Mutations (mt) in the KRAS gene are common oncogenic drivers in advanced NSCLC. The KRAS mt subtypes and certain co-mutations (co-mt) have prognostic and predictive implications. New drugs promise to uniquely target KRAS G12C, raising interest in studying the unique clinical and molecular characteristics of this subtype. Methods: We retrospectively identified 56 KRAS mt advanced NSCLC patients (pts) that underwent Next-Generation Sequencing (NGS) from 06/2015 to 12/2020. We used a commercial NGS panel that tested for > 300 genomic alterations, including substitutions, insertions, deletions and copy number alterations. For statistical analysis, we divided the patients into G12C and non-G12C groups. Results: In our cohort of 56 KRAS mt pts, median age was 67 (range: 58-74) with a predominance of females (63%) and heavy smokers (89%). KRAS G12C was the most common subtype 38%; G12V 14%, G12D 11%, G12S 11%, G13D 7% and others < 5% each. G12C, G12D and G13D groups had a higher proportion of PD-L1+ tumors (84%, 100%, and 100% respectively, p = 0.02) compared to other KRAS subtypes. Pts in G12C group were on average older (median age 71 vs 61, p = 0.02) than non-G12C. Most frequent co-mt in G12C were TP53 (33%), STK11 (29%), TET2 (19%), RB1 (14%), CDKN2A/B (14%) , MCL1 (14%) and ASXL1 (14%) ; for non-G12C, they were TP53 (54%), CDKN2A/B (37%), STK11 (34%) and RBM10 (17%). CDKN2A/B co-mt (37% vs 14%, p = 0.08) was significantly more frequent in non-G12C group and TET2 in G12C (19% vs 0%, p = 0.016). Non-G12C group more frequently had high TMB (17% vs 0%, p = 0.07) compared to G12C. No difference in survival was seen between G12C and non-G12C groups. We observed no difference in PFS (p = 0.31) or OS (p = 0.64) between smokers and no/light-smokers with KRAS mt. Co-mt with KEAP1 and SMARCA4 were significantly associated with survival in KRAS mt. Compared to KRAS+/KEAP1wt, KRAS +/ KEAP1+ pts had poor PFS (median 1.1 vs 7.5 m, p < 0.0001) and OS (1.1 vs. 27.8 m, p < 0.0001) measured from start of initial therapy. KRAS+/SMARCA4+ had worse PFS (1.0 vs 6.9 m, p < 0.0001) and OS (1.4 vs. 27.8, p = 0.0001) compared to KRAS+/SMARCA4wt. KRAS mt pts with STK11/KEAP1 that were treated with immunotherapy-based regimens had shorter PFS (1.1 vs 7.6 m, p = 0.001) and OS (1.4 vs 90.9 m, p = 0.0007) compared to those treated with chemotherapy alone. Conclusions: Our data shows that pts with KRAS co-mt with STK11/KEAP1 had worse PFS and OS with the addition of immunotherapy compared to chemotherapy alone, highlighting the potential implications of these co-mt patterns on treatment outcomes. The types of co-mts are similar between KRAS G12C and non-G12C, with the exception of CDKN2A/B (less likely) and TET2 (more likely). Larger data sets are warranted to confirm our observations and determine if these co-mts may create a predictive model for individualized therapy for KRAS mt pts, potentially independent of current predictive markers.

scholarly journals Artificial intelligence, a possible solution for agriculture and animal husbandry sector?

2021 ◽  
Vol 78 (2) ◽  
pp. 1
Author(s):  
Ancuţa ROTARU ◽  
Anamaria VÂTCĂ ◽  
Ioana POP ◽  
Luisa ANDRONIE
Keyword(s):  
Artificial Intelligence ◽  
Perception And Action ◽  
New Technologies ◽  
Animal Husbandry ◽  
Data Sets ◽  
Aggregated Data ◽  
Livestock Sector ◽  
Share Data ◽  
Closed Systems ◽  
The Voice

This paper aims at making a review of the artificial intelligence concept, its global scope from the agro-livestock sector perspective and the understanding, approach and application of this concept Romania in early 2021. Artificial intelligence is a computer science sub-field that is materialized by algorithms developed starting from the logical or mathematical models of the cognition, perception and action processes. Globally, large agricultural companies are trying to grasp concepts such as big data, artificial intelligence (AI), machine learning and analysis. These areas have moved rapidly towards the agro-livestock sector too, but most companies have not been prepared to deal thoroughly with these new technologies. It really sounds interesting, but what does it take to take the next steps? The voice of the expert says: “If we really want to have a global impact on food sustainability, production and safety, we need to think about data standards, data sharing, benchmarking and analysis on aggregated data sets. Today, we see farmers who are reluctant to share data with agritech companies that have developed closed systems, which will hinder the evolution of things” (Claudia Roessler, IT specialist, Microsoft).

AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1451-1482
Author(s):  
Bowen Lou ◽  
◽  
Lynn Wu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Drug Development ◽  
Large Scale ◽  
New Drugs ◽  
Innovation Capability ◽  
Domain Experts ◽  
Medium Level ◽  
Job Postings ◽  
Mechanism Of Impact

Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm’s ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “follow-on” drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development.

scholarly journals Big Data and Artificial Intelligence Modeling for Drug Discovery

2020 ◽  
Vol 60 (1) ◽  
pp. 573-589 ◽  
Author(s):  
Hao Zhu
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery ◽  
Data Sets ◽  
Modeling And Analysis ◽  
The Road ◽  
Drug Candidates ◽  
Modeling Studies

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

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