Drug discovery from the age of information to the age of intelligence

March 20, 2019 | 35 views

Data drives drug discovery, yet it continues to be among the biggest challenges faced by the industry.1 Experiments are often not repeatable and data interpretation is subject to the biases and limitations of human beings.

Spotlight

ARIAD Pharmaceuticals, Inc.

ARIAD Pharmaceuticals, Inc., headquartered in Cambridge, Massachusetts, is focused on discovering, developing and commercializing precision therapies for patients with rare cancers. ARIAD is working on new medicines to advance the treatment of rare forms of chronic and acute leukemia, lung cancer and other rare cancers. ARIAD utilizes computational and structural approaches to design small-molecule drugs that overcome resistance to existing cancer medicines. For additional information, visit http://www.ariad.com or follow ARIAD on Twitter (@ARIADPharm).

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MEDICAL

How to Choose a Reliable Biotech Clinical Trial Management System?

Article | August 16, 2022

Introduction The medical and life-science industries are experiencing a robust transformation with the increasing prevalence of various types of diseases, including infectious diseases, chronic disorders, and acute conditions around the world. As a result, a significant rise in demand for more effective therapeutic drugs and bionics is being witnessed, leading to a swift increase in the number of clinical trials. For a successful trial, it is important for biotech companies to ensure the data submitted to regulatory bodies regarding clinical trials is accurate, reliable, and definitive from an ethical point of view. A reliable clinical trial management system plays a vital role in collecting, monitoring, and managing clinical data. The availability of high-quality clinical data also helps clinical research institutions make efficient treatment decisions and provide proper patient care. Hence, a number of biotech companies and research organizations are focusing on leveraging innovative clinical trial management solutions to handle a large amount of data, particularly in multi-center trials, and generate reliable, high-quality, and statistically sound data from clinical trials. However, selecting the most appropriate and reliable clinical trial management system is vital for the clinical trial's success. Let's see some of the steps that will assist these firms in choosing the right CTMS. Key Steps for Selecting Right Biotech Clinical Trial Management System Prioritize Study Needs Considering and prioritizing study needs is a crucial step in choosing the most reliable clinical trial management system for biotech companies. Prioritizing helps them to identify a solution that improves the study's quality and removes uncertainty for researchers when faced with difficult choices. Hence, biotech and life-science organizations should choose a clinical trial system that is simple to use, well-organized, and suitably designed to minimize the number of clicks required to complete a task. Select CTMS with Multiple Integrations Integrated clinical trial management systems provide the best value for the companies’ funds as they guarantee the smooth functioning of research protocols. In addition, integrations are necessary to fully understand the importance and advantages of clinical trial management software for ensuring smooth transitions between site management and data collection. Biotech and clinical research should look for CTMS platforms that can integrate with electronic medical record (EMR) platforms and clinical research process content (CRPC) billing grids. This will allow them to use the same billing designations and ensure compliance while minimizing the need for duplicate processes. Ensure System Compliance and Security Clinical research organizations need to adhere to a plethora of complex regulations in order to ensure compliance with one of the most challenging environments of principles, which is information security and privacy. Security and system compliance are vital aspects of choosing the right CTMS solutions for biotech firms as they assist in building trust and form a part of the system’s duties. While selecting CTMS systems, it is essential for companies engaged in clinical research to ensure that these platforms are able to configure both, group and individual permissions, along with having a data backup and recovery plan for hosted systems. This will allow companies to assess the privacy and security implications of research and anticipate complications that may arise in each phase of the project. Assess the Scalability Choosing a scalable CTMS that can accommodate various types of fluctuations and expansions enables biotech and clinical firms to quickly adapt to fast-changing trends and demand spikes while reducing maintenance costs and enhancing user agility. As scalability also means secure and expanded data storage, these businesses should instead use SaaS solutions than manually manage an ever-growing collection of hard drives. The right CTMS ensures accommodating the firm’s availability requirements without incurring the capital costs associated with expanding a physical infrastructure. The Closing Thought A well-executed and successful clinical trial involves multiple stages and processes. Several quality controls and stringent adherence to regulations are essential for the steps, along with efficient cross-departmental processes and procedures. Incorporating the right CTMS paves the way for paperless data collection, regulatory filing, and fiscal management tools for biotech researchers and administrative personnel.

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MEDTECH

Top 3 Biotech Clinical Data Management Trends to Watch in 2022

Article | July 13, 2022

Introduction The administration of medical records and data has advanced significantly during the past few decades. Clinical data management, which was once only a small subset of biotech research organizations, has now developed into a mission-critical, specialized unit. In the late 1990s, electronic data capture (EDC) began to alter the traditional function of clinical data management. After that, the data configuration and management of data queries for the EDC system fell under the purview of clinical data management services. Today, clinical data management is not only responsible for managing the clinical data configuration and data queries but also developing and implementing data administration plans, ensuring data accuracy and completeness, and maintaining optimum data security. In recent years, as digital technologies have gained acceptance around the globe, data has become a vital aspect in decision-making across numerous industries, and the life sciences and biotechnology sectors are no exception. Using data has provided granular insights to biotech organizations, assisting them in creating breakthroughs in drug development and medical research and signifying the importance of clinical trial management systems in these medical verticals. The Biggest Biotech Clinical Data Management Trends to Know About Today The future of clinical data management is contingent upon the implementation of systems and regulations. It is imperative for all organizations participating in a medical or life science trial to have transparent rules in place for sharing and retaining patient data. Also, there is a need to have a standardized format for maintaining these records and documents related to trials. This assists biotech organizations in reducing the chances of ambiguity regarding who owns what kind of data or paperwork at any given time. Over the past couple of years, the focus of the life science and biotechnology industries has shifted towards developing more effective medications and therapies, implementing personalized treatment, and finding cures for diseases such as cancer and AIDS. In response to this, a substantial rise in the number of clinical trials is being witnessed globally. As the number of clinical trials continues to accelerate, the spending on these trials rises as well. In response to this, the worldwide cost of conducting clinical trials is anticipated to reach US$ 49.80 billion in 2022. With the transition of the world from traditional to digital, medical professionals and biotech businesses are increasingly shifting towards adopting high-tech and reliable clinical trial management systems for various applications, starting from diagnosis and clinical trials to patient data documentation. But, what are the future trends in biotechnology clinical data management? Let’s discuss. Cloud-Based Clinical Metadata Repositories Automation is emerging as a new frontier in the biotech clinical data management domain, along with other innovative technologies such as artificial intelligence and machine learning. Because of this, life science establishments are witnessing a huge shift from paper-based documentation toward data-based documentation, which is creating mountains of research, compliance, and clinical data. The growing demand for new and more effective medications and drugs is augmenting the need to expedite clinical trials. This is resulting in an increased number of initiatives aimed at optimizing clinical trial processes to prepare and launch successful trials. However, pharmaceutical and biotechnology laboratories are encountering several challenges in collecting, managing, and analyzing metadata due to its complexities. So, what is the best solution to this problem? The answer to this is cloud-based clinical metadata repositories. Clinical research facilities are leveraging advanced, all-in-one, cloud-based clinical metadata repositories to assist them in centralizing and managing metadata; increasing metadata quality, consistency, and accuracy; and speeding up clinical trial management, documentation, and compliance processes. Shift Towards Digital Solutions Electronic Case Report Form Adequate research and accurate data are crucial for a clinical trial to succeed. Whether developing new drugs, medication, or therapies; conducting life science research; or studying the latest clinical trial systems, it is best to use electronic solutions as it reduces the room for mistakes during the transition of clinical data from paper-based format. Realizing this, biotech organizations are shifting towards using electronic case report forms to speed up record retrieval, improve record security, and cut down on operational costs associated with running clinical trials. The electronic case report form assists in lowering the failure rate of the clinical trial, enhancing efficiency, and optimizing security along with improving clinical trial documentation and productivity, further driving its adoption in the medical space. Electronic Clinical Outcome Assessment Electronic clinical outcome assessment is surfacing as one of the fast-growing future trends in biotechnology. It allows clinical trial facilities to automate data entry and improve the reliability of the collected information. The technology enables clinical trial institutions to automatically record patient-provided information about side effects, symptoms, drug timing, and other aspects during the clinical trial for increased precision. It also helps these institutions analyze the results of medication or therapy in clinical trials and lets clinical researchers use medical technologies like biosensor-enabled devices, self-service applications, and medical wearables for evaluation. Hence, biotech clinical facilities are increasingly deploying advanced electronic clinical outcome assessment systems to ensure adherence to protocols and regulations. Clinical Trial Customization The success of a new drug is determined by numerous factors other than its effectiveness, safety, and creativity of its developers, such as a successful clinical trial. Each clinical trial involves a number of decision-making points, and one wrong choice in any of these aspects can jeopardize the success of the entire endeavor. A crucial component of making well-informed decisions is data management, which is a part of clinical study as a whole. Clinical trial customization is emerging as one of the most prominent biotech clinical trial management trends. Every clinical trial is unique and needs a tailored approach to be successful. With the emergence of the trend of personalized treatment around the globe, biotech and pharmaceutical organizations are adopting innovative customized clinical trial management solutions to accelerate the pace of clinical trials and approvals. This is giving clinical researchers innovative ways to come up with new medicines for patients and streamline the clinical data as per the requirements for faster approvals. What Are the Key Clinical Data Management Challenges Faced by Biotech Companies? Groundbreaking medical interventions are of no use without reliable, accurate, and extensive clinical trial data. Without the data, biotech and pharmaceutical companies will not be able to provide the assurance of safety and efficacy required to bring the medication to market. Regulatory bodies such as the Food and Drug Administration (FDA), the Medicines and Healthcare Products Regulatory Agency (MHRA), and others are putting stricter rules in place to ensure the quality of clinical data. In addition, the fast-changing clinical development environment is creating more obstacles for biotech and medical spaces to ensure the accuracy, standard, and completeness of the clinical trial data. Hence, clinical teams are spending valuable time cleaning up data instead of analyzing it. Time spent trying to figure out issues with clinical trial data is detrimental and expensive but also mission-critical. This is because a small issue in the data can lead to numerous consequences, from small delays to calamitous setbacks, making it necessary to rerun clinical trials. This problem will only get more challenging to address as the volume of data and the types of data sources continue to grow. Here are some of the major clinical data management challenges that biotech firms encounter Standardization of Clinical Metadata Stringent Regulatory Compliance Increased Clinical Trial Complexity Mid-Study Changes Why Are Clinical Data Management Systems Garnering Popularity in the Biotech Industry? With the changing regulatory and clinical landscape, biotech and pharmaceutical companies are facing several obstacles in the management of clinical data and clinical trials. In addition, regulatory agencies are moving toward integrated electronic systems, which is making it more and more important for clinical laboratories to change the format of their submissions. Because of this, several biotech clinical labs are focusing on adopting innovative laboratory solutions, such as biotech clinical data management systems, to meet the need for standardized data inputs and replace all manual ways of working with electronic systems. A clinical data management system establishes the framework for error-free data collection and high-quality data submission, resulting in speedier drug discovery and shorter time-to-market. These solutions are gaining huge traction among biotech and pharmaceutical companies, owing to their ability to effectively manage clinical data, accelerate clinical trials, and ensure compliance. Let’s see some of the features of biotech clinical data management software that are most sought after by life-science companies Controlled, standardized data repository. Centralized data analysis and administration. Reduced operational expenditures for clinical data processes. Enhanced process effectiveness. Superior submission quality Compliance with predefined standards. Clinical Data Management Systems: The Future The role of clinical data management systems is evolving at a rapid pace as the life science and medical industries continue to incorporate digital solutions for diverse operations. These systems are being used in a variety of biotech clinical settings, ranging from clinical data compliance to data science and analytics, to help them analyze large and growing volumes of clinical data. Hence, a number of high-tech medical companies are aiming at integrating innovative technologies, such as artificial intelligence and machine learning, into clinical data management software to automate clinical data management tasks, improve clinical data submission, and enhance data quality. These new biotech clinical management technologies are anticipated to help life science laboratories gain a better understanding of diseases and speed up clinical trials in the coming years. FAQ What is a clinical data management system? A clinical data management system (CDMS) is a tool used in clinical research to track, record, and manage clinical trial data across medical establishments such as biotech laboratories. What are the key functions of the biotech clinical data management system? Some of the key functions of biotech clinical data management system are Documentation of Protocols and Regulations Patient Recruitment Real-time Clinical Study Analytics Reporting Investigator Relationship Management Electronic Visit Report Why is a clinical data management system needed for clinical trials today? A clinical data management system helps shorten the time from drug development to marketing by assisting in the collection of high-quality, statistically sound, and accurate data from clinical trials.

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MEDTECH

Top 10 biotech IPOs in 2019

Article | October 7, 2022

The big question at the start of 2019 was whether the IPO window would stay open for biotech companies, particularly those seeking to pull off ever-larger IPOs at increasingly earlier stages of development. The short answer is yes—kind of. Here’s the long answer: In the words of Renaissance Capital, the IPO market had “a mostly good year.” The total number of deals fell to 159 from 192 the year before, but technology and healthcare companies were standout performers. The latter—which include biotech, medtech and diagnostics companies—led the pack, making up 43% of all IPOs in 2019. By Renaissance’s count, seven companies went public at valuations exceeding $1 billion, up from five the year before

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Cell Out? Lysate-Based Expression an Option for Personalized Meds

Article | February 18, 2020

Cell-free expression (CFE) is the practice of making a protein without using a living cell. In contrast with cell line-based methods, production is achieved using a fluid containing biological components extracted from a cell, i.e., a lysate. CFE offers potential advantages for biopharma according to Philip Probert, PhD, a senior scientist at the Centre for Process Innovation in the U.K.

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Spotlight

ARIAD Pharmaceuticals, Inc.

ARIAD Pharmaceuticals, Inc., headquartered in Cambridge, Massachusetts, is focused on discovering, developing and commercializing precision therapies for patients with rare cancers. ARIAD is working on new medicines to advance the treatment of rare forms of chronic and acute leukemia, lung cancer and other rare cancers. ARIAD utilizes computational and structural approaches to design small-molecule drugs that overcome resistance to existing cancer medicines. For additional information, visit http://www.ariad.com or follow ARIAD on Twitter (@ARIADPharm).

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MEDICAL

NiKang Therapeutics Completes $200 Million Series C Financing to Advance Highly Differentiated Small Molecules Addressing Difficult-to-Drug Targets

NiKang Therapeutics | May 31, 2021

NiKang Therapeutics Inc., a clinical-stage biotech company focused on developing innovative small molecule oncology medicines to assist patients with unmet medical needs; today announced the completion of an oversubscribed $200 million Series C financing led by Cormorant Asset Management, HBM Healthcare Investments, and Octagon Capital Advisors with participation from a premier syndicate of funds, including new investors EcoR1 Capital, Perceptive Advisors, Wellington Management, Ally Bridge Group, Pavilion Capital, funds and accounts managed by BlackRock, RA Capital Management, Surveyor Capital (a Citadel company), Samsara BioCapital, PFM Health Sciences, Invus, Janus Henderson Investors and Logos Capital. All existing investors, including CBC Group, RTW Investments, LP, Lilly Asia Ventures, Matrix Partners China, and Casdin Capital, participated in the financing. About the funding, Bing Yao, Ph.D., former CEO and chairman of Viela Bio, and Ting Jia, Ph.D., founder and chief investment officer of Octagon Capital Advisors, will join NiKang’s Board of Directors. “We are thrilled to have such an outstanding group of investors as our shareholders,” said Zhenhai Gao, Ph.D., co-founder, president, and chief executive officer of NiKang. “Their support of our vision allows us to build the world’s leading precision oncology company. We are now well-positioned to rapidly advance our pipeline into the clinic, including our differentiated HIF-2 alpha inhibitor, and to bring our company to the next level of growth.” “This financing is a testament to the quality of our science and team,” Kelsey Chen, Ph.D., MBA, chief financial officer, added. “Since joining NiKang, I have witnessed the passion and dedication of a group of talented scientists who are devoting their lives to advancing treatments for patients. We are grateful to be recognized by such a high-caliber group of investors.” “NiKang has made remarkable progress over the last eight months since our initial investment,” said Ting Jia, Ph.D., a chief investment officer of Octagon. “We are impressed by the team’s accomplishment. We believe NiKang’s unique approach to attacking difficult-to-drug targets offers promising opportunities to develop breakthrough treatments for cancer patients. We are excited to co-lead the series C financing and partner with the NiKang team to accelerate its growth.” “We are proud of what NiKang has achieved since its inception,” said Sean Cao, executive chairman of NiKang and managing director of CBC Group, which incubated the company. “The strength of this group of investors validates NiKang’s achievements and demonstrates their confidence in NiKang’s potential to grow into a leading innovative drug company.” Proceeds will be used to advance the company’s lead drug candidates into the clinic, expand the pipeline, and fund internal drug discovery programs. About NiKang Therapeutics NiKang Therapeutics is a clinical-stage biotech company focused on discovering and developing innovative small molecule oncology medicines to assist patients with unmet medical needs. Our target selection is driven by deep insights into disease biology and molecular pathways. Our discovery approach is informed by target structure biology and capitalizes on structure-based drug design. The successful implementation of our strategy enables us to rapidly and efficiently discover and advance proprietary drug candidates with the most desirable pharmacological features into clinical studies. We strive to bring transformative medicines to patients in need.

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MEDICAL

TeselaGen Biotechnology Announced the Launch of a New Protein Optimization Toolkit for Automated Biotherapeutic Drug Design and Development

TeselaGen Biotechnology | May 21, 2021

TeselaGen Biotechnology today announced the launch of a new protein optimization toolkit for biotherapeutic drug design and development, introducing significant enhancements to the company’s flagship TeselaGen® OS to form designing and developing pharmaceuticals and biotherapeutics faster and fewer expensive. The new capabilities, easily accessible via the cloud-based platform, simplify the planning of highly complex combinatorial protein libraries and support AI models for optimizing new peptides and proteins. New application programming interfaces (APIs) and integration tools have also been extended to further enhance users’ access to the new capabilities. TeselaGen integrates the facility of AI with one end-to-end platform for design, construction, data gathering, and analysis of bioproduct performance, from pharmaceuticals to food and fabrics, significantly accelerating time to plug and reducing costs. The platform’s DESIGN, BUILD, TEST, and find out modules enable researchers to effectively collaborate across an organization's development pipeline to style and build experiments, standardize and share data, and learn and preserve project results by embedding them during a machine learning model. TeselaGen’s DESIGN is an intuitive, user-interface-driven module that permits scientists to style highly complex combinatorial libraries. With this new release, the planning now supports aminoalkanoic acid parts which will be efficiently mapped to DNA. TeselaGen can then automatically generate biology protocols for efficiently synthesizing and assembling the corresponding DNA libraries. TeselaGen’s DISCOVER now supports AI models which will recommend new peptides and proteins supported by the training of supervised and unsupervised learning models. The platform also supports the modeling of unnatural amino acids and multicriteria optimization of proteins. R&D groups can utilize the TeselaGen OS to hurry the invention process. Datasets are uploaded and arranged within the platform and immediately useful for model building within TeselaGen’s DISCOVER module. TeselaGen has demonstrated that it can increase the planning and build speed of biological products and reduce the prices related to research & development by an order of magnitude. Current partnering companies are using the new capabilities for designing antibodies and optimizing their binding affinity, titer, specific productivity, immunogenicity, or other phenotypic variables of interest. Researchers also are looking to TeselaGen for rapidly engineering new vaccines - using methods like virus-like particles (VLPs), DNA, and RNA vaccines - opening the door to attacking rapidly mutating RNA and retroviruses like influenza, HCV, HIV, or coronaviruses. About TeselaGen Biotechnology TeselaGen Biotechnology has developed the primary artificial intelligence-enabled OS for biotechnology, enabling the event and commercialization of high-performance bioproducts – from pharmaceuticals to food to fabrics – faster and easier than ever. TeselaGen® connects biologists, lab technicians, and bioinformaticians so that they will collaboratively design and build experiments, organize and standardize data then test and continually learn from the info. TeselaGen has been deployed by Fortune 50 companies and emerging innovators in biopharmaceuticals, agriculture, and specialty chemicals. the corporate is privately held and based in San Francisco, California.

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AI

Iktos Partners with Kadmon to Use AI for New Drug Design

Iktos, Kadmon | May 19, 2021

Iktos, a company specializing in Artificial Intelligence for new drug design, announced today that it has signed a Research Collaboration Agreement with Kadmon, a clinical-stage biopharmaceutical company based in New York, USA, under which Iktos' generative modeling artificial intelligence (AI) technology will be used to allow the rapid and cost-effective design of novel drug candidates. Iktos will use its de novo structure-based generative modeling technologies to find novel compounds that meet a pre-defined target product profile as part of the deal, to speed up Kadmon's early-stage discovery efforts. Kadmon discovers, develops, and delivers small molecules and biologics for the treatment of human diseases. Intending to identify and develop new product candidates for significant unmet medical needs, Kadmon is expanding and incorporating novel drug discovery platforms. The AI technology developed by Iktos, which is focused on deep generative models, aids in the speed and efficiency of the drug discovery process. Iktos' technology creates virtual novel molecules that have all of the properties of a successful drug molecule automatically. This approach, which has been validated by Iktos' other collaborations, is an innovative approach to one of the most difficult problems in drug design: finding molecules that meet several important drug criteria at the same time, such as potency, selectivity, safety, and project-specific properties. Iktos' technology enables the creation of new hits with optimal protein-ligand interactions in early-stage discovery projects, as predicted by molecular modeling technology. This technique allows for a one-of-a-kind discovery of chemical space, as well as the development of innovative molecule designs with greater Freedom to Operate. Furthermore, allowing multi-parametric in silico optimization from the start of a project greatly reduces the hit finding and hit-to-lead optimization phases. About Iktos Iktos, a French start-up founded in October 2016, specializes in the development of artificial intelligence technologies for chemical research, especially medicinal chemistry, and new drug design. Iktos is working on a proprietary and innovative approach focused on deep learning generative models that allow users to build molecules in silico that follow all of the performance criteria of a small molecule discovery project using existing evidence. Iktos technology allows for significant efficiency gains in upstream pharmaceutical R&D. Iktos' software is utilized as both professional services and a SaaS software platform, Makya. Spaya, a synthesis planning software built on Iktos' proprietary AI technology for retrosynthesis, is also in the works.

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MEDICAL

NiKang Therapeutics Completes $200 Million Series C Financing to Advance Highly Differentiated Small Molecules Addressing Difficult-to-Drug Targets

NiKang Therapeutics | May 31, 2021

NiKang Therapeutics Inc., a clinical-stage biotech company focused on developing innovative small molecule oncology medicines to assist patients with unmet medical needs; today announced the completion of an oversubscribed $200 million Series C financing led by Cormorant Asset Management, HBM Healthcare Investments, and Octagon Capital Advisors with participation from a premier syndicate of funds, including new investors EcoR1 Capital, Perceptive Advisors, Wellington Management, Ally Bridge Group, Pavilion Capital, funds and accounts managed by BlackRock, RA Capital Management, Surveyor Capital (a Citadel company), Samsara BioCapital, PFM Health Sciences, Invus, Janus Henderson Investors and Logos Capital. All existing investors, including CBC Group, RTW Investments, LP, Lilly Asia Ventures, Matrix Partners China, and Casdin Capital, participated in the financing. About the funding, Bing Yao, Ph.D., former CEO and chairman of Viela Bio, and Ting Jia, Ph.D., founder and chief investment officer of Octagon Capital Advisors, will join NiKang’s Board of Directors. “We are thrilled to have such an outstanding group of investors as our shareholders,” said Zhenhai Gao, Ph.D., co-founder, president, and chief executive officer of NiKang. “Their support of our vision allows us to build the world’s leading precision oncology company. We are now well-positioned to rapidly advance our pipeline into the clinic, including our differentiated HIF-2 alpha inhibitor, and to bring our company to the next level of growth.” “This financing is a testament to the quality of our science and team,” Kelsey Chen, Ph.D., MBA, chief financial officer, added. “Since joining NiKang, I have witnessed the passion and dedication of a group of talented scientists who are devoting their lives to advancing treatments for patients. We are grateful to be recognized by such a high-caliber group of investors.” “NiKang has made remarkable progress over the last eight months since our initial investment,” said Ting Jia, Ph.D., a chief investment officer of Octagon. “We are impressed by the team’s accomplishment. We believe NiKang’s unique approach to attacking difficult-to-drug targets offers promising opportunities to develop breakthrough treatments for cancer patients. We are excited to co-lead the series C financing and partner with the NiKang team to accelerate its growth.” “We are proud of what NiKang has achieved since its inception,” said Sean Cao, executive chairman of NiKang and managing director of CBC Group, which incubated the company. “The strength of this group of investors validates NiKang’s achievements and demonstrates their confidence in NiKang’s potential to grow into a leading innovative drug company.” Proceeds will be used to advance the company’s lead drug candidates into the clinic, expand the pipeline, and fund internal drug discovery programs. About NiKang Therapeutics NiKang Therapeutics is a clinical-stage biotech company focused on discovering and developing innovative small molecule oncology medicines to assist patients with unmet medical needs. Our target selection is driven by deep insights into disease biology and molecular pathways. Our discovery approach is informed by target structure biology and capitalizes on structure-based drug design. The successful implementation of our strategy enables us to rapidly and efficiently discover and advance proprietary drug candidates with the most desirable pharmacological features into clinical studies. We strive to bring transformative medicines to patients in need.

Read More

MEDICAL

TeselaGen Biotechnology Announced the Launch of a New Protein Optimization Toolkit for Automated Biotherapeutic Drug Design and Development

TeselaGen Biotechnology | May 21, 2021

TeselaGen Biotechnology today announced the launch of a new protein optimization toolkit for biotherapeutic drug design and development, introducing significant enhancements to the company’s flagship TeselaGen® OS to form designing and developing pharmaceuticals and biotherapeutics faster and fewer expensive. The new capabilities, easily accessible via the cloud-based platform, simplify the planning of highly complex combinatorial protein libraries and support AI models for optimizing new peptides and proteins. New application programming interfaces (APIs) and integration tools have also been extended to further enhance users’ access to the new capabilities. TeselaGen integrates the facility of AI with one end-to-end platform for design, construction, data gathering, and analysis of bioproduct performance, from pharmaceuticals to food and fabrics, significantly accelerating time to plug and reducing costs. The platform’s DESIGN, BUILD, TEST, and find out modules enable researchers to effectively collaborate across an organization's development pipeline to style and build experiments, standardize and share data, and learn and preserve project results by embedding them during a machine learning model. TeselaGen’s DESIGN is an intuitive, user-interface-driven module that permits scientists to style highly complex combinatorial libraries. With this new release, the planning now supports aminoalkanoic acid parts which will be efficiently mapped to DNA. TeselaGen can then automatically generate biology protocols for efficiently synthesizing and assembling the corresponding DNA libraries. TeselaGen’s DISCOVER now supports AI models which will recommend new peptides and proteins supported by the training of supervised and unsupervised learning models. The platform also supports the modeling of unnatural amino acids and multicriteria optimization of proteins. R&D groups can utilize the TeselaGen OS to hurry the invention process. Datasets are uploaded and arranged within the platform and immediately useful for model building within TeselaGen’s DISCOVER module. TeselaGen has demonstrated that it can increase the planning and build speed of biological products and reduce the prices related to research & development by an order of magnitude. Current partnering companies are using the new capabilities for designing antibodies and optimizing their binding affinity, titer, specific productivity, immunogenicity, or other phenotypic variables of interest. Researchers also are looking to TeselaGen for rapidly engineering new vaccines - using methods like virus-like particles (VLPs), DNA, and RNA vaccines - opening the door to attacking rapidly mutating RNA and retroviruses like influenza, HCV, HIV, or coronaviruses. About TeselaGen Biotechnology TeselaGen Biotechnology has developed the primary artificial intelligence-enabled OS for biotechnology, enabling the event and commercialization of high-performance bioproducts – from pharmaceuticals to food to fabrics – faster and easier than ever. TeselaGen® connects biologists, lab technicians, and bioinformaticians so that they will collaboratively design and build experiments, organize and standardize data then test and continually learn from the info. TeselaGen has been deployed by Fortune 50 companies and emerging innovators in biopharmaceuticals, agriculture, and specialty chemicals. the corporate is privately held and based in San Francisco, California.

Read More

AI

Iktos Partners with Kadmon to Use AI for New Drug Design

Iktos, Kadmon | May 19, 2021

Iktos, a company specializing in Artificial Intelligence for new drug design, announced today that it has signed a Research Collaboration Agreement with Kadmon, a clinical-stage biopharmaceutical company based in New York, USA, under which Iktos' generative modeling artificial intelligence (AI) technology will be used to allow the rapid and cost-effective design of novel drug candidates. Iktos will use its de novo structure-based generative modeling technologies to find novel compounds that meet a pre-defined target product profile as part of the deal, to speed up Kadmon's early-stage discovery efforts. Kadmon discovers, develops, and delivers small molecules and biologics for the treatment of human diseases. Intending to identify and develop new product candidates for significant unmet medical needs, Kadmon is expanding and incorporating novel drug discovery platforms. The AI technology developed by Iktos, which is focused on deep generative models, aids in the speed and efficiency of the drug discovery process. Iktos' technology creates virtual novel molecules that have all of the properties of a successful drug molecule automatically. This approach, which has been validated by Iktos' other collaborations, is an innovative approach to one of the most difficult problems in drug design: finding molecules that meet several important drug criteria at the same time, such as potency, selectivity, safety, and project-specific properties. Iktos' technology enables the creation of new hits with optimal protein-ligand interactions in early-stage discovery projects, as predicted by molecular modeling technology. This technique allows for a one-of-a-kind discovery of chemical space, as well as the development of innovative molecule designs with greater Freedom to Operate. Furthermore, allowing multi-parametric in silico optimization from the start of a project greatly reduces the hit finding and hit-to-lead optimization phases. About Iktos Iktos, a French start-up founded in October 2016, specializes in the development of artificial intelligence technologies for chemical research, especially medicinal chemistry, and new drug design. Iktos is working on a proprietary and innovative approach focused on deep learning generative models that allow users to build molecules in silico that follow all of the performance criteria of a small molecule discovery project using existing evidence. Iktos technology allows for significant efficiency gains in upstream pharmaceutical R&D. Iktos' software is utilized as both professional services and a SaaS software platform, Makya. Spaya, a synthesis planning software built on Iktos' proprietary AI technology for retrosynthesis, is also in the works.

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