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CRISPR Therapies Pipeline Insights 2021: Analysis of Key Companies, Emerging Therapies, Recent Happenings and Futuristic Trends

The leading gene-editing companies looking at commercializing CRISPR-based therapeutics are CRISPR Therapeutics, Intellia Therapeutics, and Editas Medicine. CRISPR Therapeutics has the largest market cap of the three, at $10.9B, with a clinical development program that is more advanced than those of Intellia and Editas. Editas Medicine has the smallest market cap of the three companies. Intellia has established high profile collaborations with Regeneron and Novartis.Los Angeles, USA, April 12, 2021 (GLOBE NEWSWIRE) — CRISPR Therapies Pipeline Insights 2021: Analysis of Key Companies, Emerging Therapies, Recent Happenings and Futuristic Trends The leading gene-editing companies looking at commercializing CRISPR-based therapeutics are CRISPR Therapeutics, Intellia Therapeutics, and Editas Medicine. CRISPR Therapeutics has the largest market cap of the three, at $10.9B, with a clinical development program that is more advanced than those of Intellia and Editas. Editas Medicine has the smallest market cap of the three companies. Intellia has established high profile collaborations with Regeneron and Novartis. DelveInsight’s “CRISPR Therapies Pipeline Insight” report offers a broad view of available CRISPR therapies in the market, pipeline CRISPR therapies, their MoA, RoA, key companies working in the domain and competitive assessment. Some of the key takeaways from the CRISPR Therapies Pipeline Report: CRISPR/Cas9 has the potential to transform the healthcare industry by proving to be a disruptive innovation by switching on/ off any particular gene.CRISPR technology has broad potential in the field of cancer and genetic diseases. The key drivers of the CRISPR technology market are: increase in investment by government and private institutions on research and development of genome editing, rise in prevalence of genetic disorders, and increase in the application of CRISPR/Cas9 technology to improve crop production.However, ethical issues associated with CRISPR technology and lack of skilled personnel can hinder the growth of the global CRISPR technology market.Some of the key players in the field of CRISPR technology are Vertex Pharmaceuticals; Excision Biotherapeutic; CRISPR Therapeutics; Intellia Therapeutics; Editas Medicine; Thermo Fisher Scientific; Integrated DNA Technologies, Inc. (Danaher); GenScript Biotech Corporation; GeneCopoeia; Applied StemCell; Casebia Therapeutics; Modalis Therapeutics; Mustang Bio; Sarepta Therapeutics; Origene Technologies; ASC Therapeutics; Emendo Biotherapeutics; Precision Biosciences; Horizon Discovery; Synthego Corporation; Agilent Technologies; Evotec A.G; Beam Therapeutics; New England Biolabs; Novartis; Caribou Biosciences; Addgene among othersIn November 2019, Vertex Pharmaceuticals entered into a strategic research collaboration with Molecular Templates (MTEM) to discover and develop targeted conditioning regimens to enhance the haematopoietic stem cell transplant process, including transplants conducted as part of treatment with ex vivo CRISPR/Cas9 CTX 001 gene-editing therapy. The collaboration seeks to discover a new conditioning regimen utilizing MTEM’s engineered toxin body (ETB) platform designed to specifically target and remove specific cells to enable successful engraftment of new cells.Gene editing technologies such as CRISPR produces good results in the screening phase of drug discovery by using the CRISPR–associated protein 9 (Cas9) enzyme and guide RNA to edit the genome at a particular point. In November 2019, German drug discovery and development company, Evotec SE received the CRISPR gene-editing technology license from Merck to discover and test new drugs. Interested in knowing more? Request for the sample @ CRISPR Therapies in the Pipeline The report underlines the present unmet needs in the market, driving factors and market constraints, along with the holistic view of the inactive therapeutics (comprising dormant and terminated products) with the reasons behind their downfall, detailed insights into the structure and gene editing tool of the pipeline CRISPR therapies to help clients gauge the opportunities and risks in the market. In the News CRISPR Therapeutics has announced its participation in the 20th Annual Needham Virtual Healthcare Conference on April 12, 2021, at 9:30 a.m. ET.Scribe Therapeutics recently announced a USD 100 million series B funding round to help the company achieve its mission in developing molecules through its “Crispr by design” platform. The company has also inked a deal with Biogen to design Crispr-based genetic medicines for diseases such as amyotrophic lateral sclerosis (ALS).Intellia Therapeutics announced the European Commission (EC) approval of orphan drug designation to its NTLA-2001, being developed for a rare condition transthyretin amyloidosis (ATTR). Editas Medicine plans to advance its landmark Brilliance trial, the first-ever in vivo gene editing program investigating EDIT-101 for Leber Congenital Amaurosis 10 (LCA10). Know what is happening in the CRISPR Pipeline Therapies @ CRISPR Pipeline Recent Happenings What is CRISPR? CRISPRs (Clusters of Regularly Interspaced Short Palindromic Repeats) are specialized stretches of DNA and are a shorthand for ‘’CRISPR-Cas9’’, which are transcribed by the bacteria to RNA stretches during viral infections. The same CRISPR technology can be leveraged to identify, alter and modify the DNA sequences and genomes. The technique is used to correct genetic defects, prevent the spread of disease by altering the genetic sequence, improving crop viability and durability, and so on without affecting the functions of other genes. Want to learn more about the leading candidates in different clinical stages of trials? Reach out @ CRISPR Emerging Therapies and Key Companies At a Glance: Emerging CRISPR Therapies, RoA, MoA and Companies DrugCompanyCollaborationClinical PhaseTarget IndicationRoAEBT-101Excision BiotherapeuticTemple UniversityPre-clinicalHIV-1 infectionsIntravenousLCA10 programEditas TherapeuticsNAPre-clinicalUsher syndromesNALBP-EC01Locus BiosciencesCARB-XPhase IUrinary tract infectionsParenteralCTX-130CRISPR TherapeuticsNAPhase IDiffuse large B cell lymphoma; T-cell lymphomaIntravenousCTX001CRISPR TherapeuticsVertexPhase I/IIBeta-thalassaemia; Sickle cell anaemiaIntravenousEDIT-101Editas MedicineNAPhase I/IILeber congenital amaurosisSubretinal injectionNTLA-2001Intellia TherapeuticsRegeneronPhase ITransthyretin-related hereditary amyloidosisIntravenous Know more about budding CRISPR therapies projected to transform the landscape @ Emerging CRISPR Therapeutics and Market Scenario CRISPR Therapeutic Assessment The CRISPR Therapies Pipeline report proffers comprehensive insights into active pipeline assets segmented by Stage, Product Type, Route of Administration, Molecule Type, Target and Indications of various drugs. By Product Type MonoCombination By Stage Discovery Pre-clinicalPhase IPhase IIPhase IIIPre-registration By Route of Administration OralIntravenousInhalationSubcutaneous By Mechanism of Action Gene modulators; Prealbumin inhibitorsGene transferenceCell replacements By Targets ProteaseImmune SystemMultiple Kinase By Stage and Route of Administration By Stage and Product Type To know more, Visit CRISPR CAS-9 Technology and Emerging Trends Scope of the report Coverage: GlobalKey Players: Vertex Pharmaceuticals; Excision Biotherapeutic; CRISPR Therapeutics; Intellia Therapeutics; Editas Medicine; Thermo Fisher Scientific; Integrated DNA Technologies, Inc. (Danaher); GenScript Biotech Corporation; GeneCopoeia; Applied StemCell; Casebia Therapeutics; Modalis Therapeutics; Mustang Bio; Sarepta Therapeutics; Origene Technologies; ASC Therapeutics; Emendo Biotherapeutics; Precision Biosciences; Horizon Discovery; Synthego Corporation; Agilent Technologies; Evotec A.G; Beam Therapeutics; New England Biolabs; Novartis; Caribou Biosciences; Addgene; and many others.Key CRISPR Pipeline Therapies: CTX 120; CTX 110; OTQ 923; EDIT 301; NTLA 2001; ASC 618; EBT-101; LCA10 program; LBP-EC01; CTX-130; EDIT-101 among others. Learn more about the scope and highlights of the report @ CRISPR Pipeline Emerging Drug Pipeline Key Questions Answered in the Report How many companies are developing drugs based on CRISPR technology?How many CRISPR technology-based drugs are being developed by each company?How many CRISPR technology emerging drugs are in mid-stage, and late-stage of development?What are the key collaborations (Industry–Industry, Industry–Academia), Mergers and acquisitions, licensing activities related to the CRISPR technology?What are the recent trends, drug types and novel technologies developed to overcome the limitation of existing therapies?What are the clinical studies going on for CRISPR technology and its status?What are the key designations that have been granted to the emerging drugs? Got queries? Get in touch @ CRISPR Technology and Pipeline Therapies Table of Contents 1Introduction2Executive Summary3CRISPR Therapies Overview4Competitive Landscape – Active Drugs5CRISPR Therapies Pipeline Therapeutics6CRISPR Therapies Preclinical Stage Products7In-depth Commercial CRISPR Therapeutics Assessment8Late Stage CRISPR Pipeline Products (Phase III and Preregistration)9Mid-Stage CRISPR Therapies Pipeline Products (Phase II)10Pre-clinical and Discovery Stage CRISPR Pipeline Therapies 11Inactive CRISPR Pipeline Products12CRISPR Therapies Key Companies13CRISPR Therapies Key Products14Company-University Collaborations (Licensing/Partnering) Analysis15CRISPR Therapies Market Drivers and Barriers16CRISPR Therapies Future Perspectives and Conclusion17CRISPR Pipeline Therapies: Analyst Views18Appendix Know more about report offerings @ CRISPR Pipeline Insights Related Reports Adeno Associated Virus Vectors In Gene Therapy MarketDelveInsight’s “Adeno-Associated Virus Vectors in Gene Therapy – Market Insights, Epidemiology, and Market Forecast-2030” report. Gene Therapy In Oncology Innovation To Commercialization Competitive LandscapeDelveInsight’’s “Gene Therapies in Oncology – Innovation to Commercialization: Competitive Landscape, Technological Advancements, Market Opportunities & Future Directions, 2018” report. Complicated Urinary Tract Infections MarketDelveInsight’s “Complicated Urinary Tract Infections – Market Insights, Epidemiology, and Market Forecast-2030” report. Usher Syndrome Type 2 MarketDelveInsight’s “Usher Syndrome Type 2 – Market Insights, Epidemiology, and Market Forecast-2030” report. Diffuse Large B Cell Lymphoma MarketDelveInsight’s “Diffuse Large B-cell Lymphoma – Market Insights, Epidemiology, and Market Forecast-2030” report. Beta Thalassemia Thal MarketDelveInsight’s “Beta-thalassemia (B-thal) – Market Insights, Epidemiology, and Market Forecast-2030” report. Sickle Cell Disease MarketDelveInsight’s “Sickle Cell Disease – Market Insights, Epidemiology, and Market Forecast-2030” report. Gene and Cell Therapies in CNS DisordersAnalysis of the key companies in the domain is BrainStorm Cell Therapeutics, Helixmith, Q therapeutics, Neuroplast, StemCyte, Axovant, Libella Gene Therapeutics, Voyager Therapeutics and many others. CRISPR Gene-Editing and Stem-Cell Technology About DelveInsight DelveInsight is a leading Business Consultant and Market Research firm focused exclusively on life sciences. It supports Pharma companies by providing end-to-end comprehensive solutions to improve their performance. Get hassle-free access to all the healthcare and pharma market research reports through our subscription-based platform PharmDelve. CONTACT: Contact Us Shruti Thakur +1(919)321-6187

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Meet the Developers – Linux Kernel Team (David Vernet)





Credit: Larry Ewing ( and The GIMP for the original design of Tux the penguin.


For today’s interview, we have David Vernet, a core systems engineer on the Kernel team at Meta. He works on the BPF (Berkeley Packet Filter) and the Linux kernel scheduler. This series highlights Meta Software Engineers who contribute to the Linux kernel. The Meta Linux Kernel team works with the broader Linux community to add new features to the kernel and makes sure that the kernel works well in Meta production data centers. Engineers on the team work with peers in the industry to make the kernel better for Meta’s workloads and to make Linux better for everyone.

Tell us about yourself.

I’m a systems engineer who’s spent a good chunk of his career in the kernel space, and some time in the user-space as well working on a microkernel. Right now, I’m focusing most of my time on BPF and the Linux kernel scheduler.

I started my career as a web developer after getting a degree in math. After going to grad school, I realized that I was happiest when hacking on low-level systems and figuring out how computers work.

As a kernel developer at Meta, what does your typical day look like?

I’m not a maintainer of any subsystems in the kernel, so my typical day is filled with almost exclusively coding and engineering. That being said, participating in the upstream Linux kernel community is one of the coolest parts of being on the kernel team, so I still spend some time reading over upstream discussions. A typical day goes something like this:

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  1. Read over some of the discussions taking place on various upstream lists, such as BPF and mm. I usually spend about 30-60 minutes or so per day on this, though it depends on the day.

  2. Hack on the project that I’m working on. Lately, that’s adding a user-space ringbuffer map type to BPF.

  3. Work on drafting an article for

What have you been excited about or incredibly proud of lately?

I recently submitted a patch-set to enable a new map type in BPF. This allows user-space to publish messages to BPF programs in the kernel over the ringbuffer. This map type is exciting because it sets the stage to enable frameworks for user-space to drive logic in BPF programs in a performant way.

Is there something especially exciting about being a kernel developer at a company like Meta?

The Meta kernel team has a strong upstream-first culture. Bug fixes that we find in our Meta kernel, and features that we’d like to add, are almost always first submitted to the upstream kernel, and then they are backported to our internal kernel.

Do you have a favorite part of the kernel dev life cycle?

I enjoy architecting and designing APIs. Kernel code can never crash and needs to be able to run forever. I find it gratifying to architect systems in the kernel that make it easy to reason about correctness and robustness and provide intuitive APIs that make it easy for other parts of the kernel to use your code.

I also enjoy iterating with the upstream community. It’s great that your patches have a whole community of people looking at them to help you find bugs in your code and suggest improvements that you may never have considered on your own. A lot of people find this process to be cumbersome, but I find that it’s a small price to pay for what you get out of it.

Tell us a bit about the topic you presented at the Linux Plumbers Conference this year.

We presented the live patch feature in the Linux kernel, describing how we have utilized it at Meta and how our hyper-scale has shown some unique challenges with the feature.

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What are some of the misconceptions about kernel or open source software development that you have encountered in your career?

The biggest misconception is that it’s an exclusive, invite-only club to contribute to the Linux kernel. You certainly must understand operating systems to be an effective contributor and be ready to receive constructive criticism when there is scope for improvement in your code. Still, the community always welcomes people who come in with an open mind and want to contribute.

What resources are helpful in getting started in kernel development?

There is a lot of information out there that people have written on how to get integrated into the Linux kernel community. I wrote a blog post on how to get plugged into Linux kernel upstream mailing list discussions, and another on how to submit your first patch. There is also a video on writing and submitting your first Linux kernel patch from Greg Kroah-Hartman.

In terms of resources to learn about the kernel itself, there are many resources and books, such as:

Where can people find you and follow your work?

I have a blog where I talk about my experiences as a systems engineer: I publish articles that range from topics that are totally newcomer friendly to more advanced topics that discuss kernel code in more detail. Feel free to check it out and let me know if there’s anything you’d like me to discuss.

To learn more about Meta Open Source, visit our open source site, subscribe to our YouTube channel, or follow us on Twitter, Facebook and LinkedIn.

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Get started with WhatsApp Business Platform in Minutes with Postman





Our collaboration brings tools you already use to WhatsApp Business Platforms APIs

Postman is a best-in-class API platform used by 20M developers worldwide. Using Postman simplifies each step of the API lifecycle and streamlines collaboration.

Postman’s strong platform and broad adoption in the developer community made deciding to work with Postman to deliver a robust developer experience an easy decision for our WhatsApp Business Platform product team.

What Postman means for your WhatsApp projects

The benefits of this collaboration for developers are clear – you can easily leverage Postman’s platform with your Meta projects to onboard, collaborate, and contribute towards documentation and best practices as you build out your integrations.

Fast Onboarding

The WhatsApp team is able to offer, via Postman, an API collection that pre-fills environment variables and walks you through your initial test requests – helping developers dive right in to using the Cloud API. Our product managers show you how easy it is to get started with Postman in this session from Conversations:

Foster Collaboration

The public Postman workspace fosters collaboration – allowing environments, collections, and documentation augmentation to happen in one place.

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Enhance Documentation

Postman’s API documentation tools augment our own documentation and allows developers to contribute directly to the community’s shared knowledge, building a strong reference library for all developers and encouraging new, innovative use cases.

The Results

Working with Postman from the beginning helps create a developer-friendly experience for the WhatsApp Business Platform – allowing you to get started quickly, build community, and share knowledge.

Want to know more about our partnership with Postman? Check out their case study, follow along with the video above, or dive right into the Postman Workspace for the WhatsApp Business Platform.

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Summer of open source: building more efficient AI with PyTorch





Note: Special thanks to Less Wright, Partner Engineer, Meta AI, for review of and additional insights into the post.

This post on creating efficient artificial intelligence (AI) is the second in the “Summer of open source” series. This series aims to provide a handful of useful resources and learning content in areas where open source projects are creating impact across Meta and beyond. Follow along as we explore other areas where Meta Open Source is moving the industry forward by sharing innovative, scalable tools.

PyTorch: from foundational technology to foundation

Since its initial release in 2016, PyTorch has been widely used in the deep learning community, and its roots in research are now consistently expanding for use in production scenarios. In an exciting time for machine learning (ML) and artificial intelligence (AI), where novel methods and use cases for AI models continue to expand, PyTorch has reached the next chapter in its history as it moves to the newly established, independent PyTorch Foundation under the Linux Foundation umbrella. The foundation is made up of a diverse governing board including representatives from AMD, Amazon Web Services, Google Cloud, Microsoft Azure and Nvidia, and the board is intended to expand over time. The mission includes driving adoption of AI tooling through vendor-neutral projects and making open source tools, libraries and other components accessible to everyone. The move to the foundation will also enable PyTorch and its open source community to continue to accelerate the path from prototyping to production for AI and ML.

Streamlining AI processes with Meta open source

PyTorch is a great example of the power of open source. As one of the early open source deep learning frameworks, PyTorch has allowed people from across disciplines to experiment with deep learning and apply their work in wide-ranging fields. PyTorch supports everything from experiments in search applications to autonomous vehicle development to ground-penetrating radar, and these are only a few of its more recent applications. Pairing a versatile library of AI tools with the open source community unlocks the ability to quickly iterate on and adapt technology at scale for many different uses.

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As AI is being implemented more broadly, models are trending up in size to tackle more complex problems, but this also means that the resources needed to train these models have increased substantially. Fortunately, many folks in the developer community have recognized the need for models to use fewer resources—both from a practical and environmental standpoint. This post will explore why quantization and other types of model compression can be a catalyst for efficient AI.

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Establishing a baseline for using PyTorch

Most of this post explores some intermediate and advanced features of PyTorch. If you are a beginner that is looking to get started, or an expert that is currently using another library, it’s easiest to get started with some basics. Check out the beginner’s guide to PyTorch, which includes an introduction to a complete ML workflow using the Fashion MNIST dataset.

Here are some other resources that you might check out if you’re new to PyTorch:

  • PyTorch Community Stories: Learn how PyTorch is making an impact across different industries like agriculture, education, travel and others
  • PyTorch Beginner Series: Explore a video playlist of fundamental techniques including getting started with tensors, building models, training and inference in PyTorch.

Quantization: Applying time-tested techniques to AI

There are many pathways to making AI more efficient. Codesigning hardware and software to optimize for AI can be highly effective, but bespoke hardware-software solutions take considerable time and resources to develop. Creating faster and smaller architectures is another path to efficiency, but many of these architectures suffer from accuracy loss when compared to larger models, at least for the time being. A simpler approach is to find ways of reducing the resources that are needed to train and serve existing models. In PyTorch, one way to do that is through model compression using quantization.

Quantization is a mathematical technique that has been used to create lossy digital music files and convert analog signals to digital ones. By executing mathematical calculations with reduced precision, quantization allows for significantly higher performance on many hardware platforms. So why use quantization to make AI more efficient? Results show that in certain cases, using this relatively simple technique can result in dramatic speedups (2-4 times) for model inference.

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The parameters that make up a deep learning model are typically decimal numbers in floating point (FP) precision; each parameter requires either 16 bits or 32 bits of memory. When using quantization, numbers are often converted to INT4 or INT8, which occupy only 4 or 8 bits. This reduces how much memory models require. Additionally, chip manufacturers include special arithmetic that makes operations using integers faster than using decimals.

There are 3 methods of quantization that can be used for training models: dynamic, static and quantize-aware training (QAT). A brief overview of the benefits and weaknesses is described in the table below. To learn how to implement each of these in your AI workflows, read the Practical Quantization in PyTorch blog post.

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Quantization Method




  • Easy to use with only one API call
  • More robust to distribution drift resulting in slightly higher accuracy
  • Works well for long short-term memory (LSTM) and Transformer models

Additional overhead in every forward pass

Static (also known as PTQ)

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  • Faster inference than dynamic quantization by eliminating overhead

May need regular recalibration for distribution drift

Quantize-Aware Training (QAT)

  • Higher accuracy than static quantization
  • Faster inference than dynamic

High computational cost

Additional features for speeding up your AI workflow

Quantization isn’t the only way to make PyTorch-powered AI more efficient. Features are updated regularly, and below are a few other ways that PyTorch can improve AI workflows:

  • Inference mode: This mode can be used for writing PyTorch code if you’re only using the code for running inference. Inference mode changes some of the assumptions when working with tensors to speed up inference. By telling PyTorch that you won’t use tensors for certain applications later (in this case, autograd), it adjusts to make code run faster in these specific scenarios.

  • Low precision: Quantization works only at inference time, that is, after you have trained your model. For the training process itself, PyTorch uses AMP, or automatic mixed precision training, to find the best format based on which tensors are used (FP16, FP32 or BF16). Low-precision deep learning in PyTorch has several advantages. It can help lower the size of a model, reduce the memory that is required to train models and decrease the power that is needed to run models. To learn more, check out this tutorial for using AMP with CUDA-capable GPUs.

  • Channels last: When it comes to vision models, NHWC, otherwise known as channels-last, is a faster tensor memory format in PyTorch. Having data stored in the channels-last format accelerates operations in PyTorch. Formatting input tensors as channels-last reduces the overhead that is needed for conversion between different format types, resulting in faster inference.

  • Optimize for inference: This TorchScript prototype implements some generic optimizations that should speed up models in all environments, and it can also prepare models for inference with build-specific settings. Primary use cases include vision models on CPUs (and GPUs) at this point. Since this is a prototype, it’s possible that you may run into issues. Raise an issue that occurs on the PyTorch GitHub repository.

Unlocking new potential in PyTorch

Novel methods for accelerating AI workflows are regularly explored on the PyTorch blog. It’s a great place to keep up with techniques like the recent BetterTransformer, which increases speedup and throughput in Transformer models by up to 2 times for common execution scenarios. If you’re interested in learning how to implement specific features in PyTorch, the recipes page allows you to search by categories like model optimization, distributed training and interpretability. This post is only a sampling of how tools like PyTorch are moving open source and AI forward.

To stay up to date with the latest in Meta Open Source for artificial intelligence and machine learning, visit our open source site, subscribe to our YouTube channel, or follow us on Facebook, Twitter and LinkedIn.

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