How Digital Pathology is Advancing Biomarker Identification & Drug Discovery
For over 100 years pathologists have been using microscopes to study disease at a cellular level and despite improvements in the objective lens and light…
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For over 100 years pathologists have been using microscopes to study disease at a cellular level and despite improvements in the objective lens and light sources, there have been few changes to the techniques during this time.
In the 1990’s, laboratories began attaching cameras to microscopes and taking still images of small fields of use for publication and training purposes. Then in the late 90’s whole slide imaging began to emerge and the term ‘digital pathology’ was coined.
“Initially it took almost a day to scan one slide because of how slow the technology was, but nowadays, it can take just one to two minutes to get the whole slide scanned,” said Vipul Baxi, Scientific Senior Director of Digital Pathology at Bristol Myers Squibb (BMS).
He explained that digital pathology broadly falls into two categories. The first involves using computer-based virtual slides that allow a pathologist to review on a screen exactly what they would normally look at under a microscope.
This method has been particularly valuable during the COVID-19 pandemic, allowing pathologists to examine slides remotely. Indeed, in April 2020 the FDA relaxed their regulations1, which normally prevent pathologists from making primary diagnoses outside of the laboratory. The regulatory body is temporarily allowing pathologists to access images of patient tissue remotely through a secure VPN high-speed internet connection and make definitive diagnoses via a web-based browser using a monitor that meets specified minimum requirements.
Artificial intelligence
The second category of digital pathology takes the technology further by adding computer vision and image analysis algorithms on top. The algorithms are developed using state-of-the-art artificial intelligence (AI) techniques, including various machine learning (ML) and deep learning (DL) models.
This technology can “mimic what the pathologist is doing [then also] go a step beyond and uncover things that the pathologist may see but may not be able to quantify as robustly, or as consistently,” Baxi explained.
Baxi joined BMS 6 years ago to build the internal digital pathology infrastructure and establish external strategic partnerships that the global biopharmaceutical company is now deploying within its translational research for novel biomarker discovery. His team began with routine digitalization of tissue specimen slides collected during the trials and they are now developing algorithms to enhance the analysis of biomarkers in a quantitative manner.
The next step, he said, will be to develop advanced AI techniques to uncover computationally derived biomarkers that go beyond what a human pathologist could visually quantify, incorporating contextual features that can better characterize the disease biology and potentially determine the appropriate treatment for patients.
Research from 2016 published in Nature Reviews Drug Discovery2 suggests that around just 10% of drug development projects reach the approval stage, so any technology that boosts this proportion by improving clinical trial success rates will be valuable to the pharmaceutical industry.
A 2021 survey3 by the analytics firm GlobalData compounds this viewpoint, showing that AI was expected to be the emerging technology that would have the greatest impact on the pharmaceutical industry in that year.
Geographic contextualization
One particular use of AI that is gaining momentum in digital pathology is the geographic contextualization of data using spatial algorithms. Baxi explained that geographic contextualization is “not just quantifying the biomarker on a patient, it’s understanding where the biomarker expression is located in the tumor microenvironment, how the cells are spatially oriented, and how they’re interacting with each other.”
This information cannot be determined using traditional pathology methods and will help researchers to understand how a disease is progressing or why a patient might respond to one particular treatment over another.
Multiplexed staining
Another area of progress that could help improve the success rate of clinical trials is within staining technology. It is now possible to stain slides for multiple markers and look at different cell types on a single tissue specimen. Analysis of these digitized multiplexed slides can then be carried out by a digital algorithm that is capable of processing the large volumes of information available all at once. This is advantageous because tissue samples can be limited, and it is not always possible to stain for every marker of interest using standard techniques.
Streamlining drug discovery
Taken together, these new methods can help streamline the drug discovery process in a number of ways. Baxi sets them out in a review article recently published in Modern Pathology4 beginning with target identification, followed by indication selection, where digital pathology can assist with selecting which biomarkers to target. Tumor profiling can then help to understand the mechanism of action, while comparing pre- and post-treatment samples can shed light on pharmacodynamics.
Digital pathology can also be used to stratify patients within trials and then finally as a companion diagnostic to identify patients most likely to benefit from a particular treatment.
Global healthcare company GSK started building AI models and computational pathology around 2 years ago and now have the largest AI group, with around 110 members of staff, in the pharmaceutical industry.
SVP and Global Head, GSK
Kim Branson, Senior Vice President and Global Head, AI and ML, GSK, believes that digital pathology “is going to be absolutely transformative” for clinical trials and drug discovery because it “allows us to do different things we couldn’t do before.”
One such thing is spatial transcriptomics, he said, which involves looking at mRNA expression in various cell types on the same slide. This can be then be taken up a level and applied to 3D images of whole tumor samples, such as those produced by Alpenglow Biosciences.
3D digital pathology
3D images can better capture the complexity of human tumors, including influences from the surrounding microenvironment, than cells cultured in 2D. GSK are now working on a 5-year project with King’s College London5 that will use these tumor models alongside digital pathology and AI to develop personalized immuno-oncology treatments for a number of solid cancers, including lung, gastrointestinal, and women’s cancers.
The study will use tumor samples to culture and grow a 3D “biological twin” that will then be digitalized and allow researchers to test multiple drugs at multiple doses and timepoints.
Another collaboration that GSK have recently announced is with PathAI, who provide various AI-powered algorithms to 80% of the top global pharmaceutical companies for use in translational research, clinical trials, and clinical diagnostic development.
Non-alcoholic steatohepatitis
One of those algorithms, which Branson describes as “world class” is for non-alcoholic steatohepatitis (NASH), which is an advanced form of non-alcoholic fatty liver disease characterized by liver inflammation that can lead to fibrosis, cirrhosis, and ultimately liver cancer.
GSK has a drug candidate for NASH, so they will be using PathAI’s model to support their clinical trials. They will also be deploying some of their other computational pathology algorithms on PathAI’s platform for clinical trials in other disease areas such as oncology.
Andy Beck, CEO and co-founder of PathAI, says that “there is a growing need within the pharmaceutical industry to get more accurate and reliable data from pathology samples in a way that is repeatable, scalable, and quantitative. Digital image analysis via AI can help reduce the variability that is associated with manual pathology and improve the accuracy of biomarker measurement.”
He adds that AI also allows for more complex analysis of large amounts of data and has “created a new pathway for biomarker discovery, as it can improve the assessment of predictive biomarkers in tissue biopsies leading to new, impactful discoveries with therapeutic indications.”
Commercial pathology
Aside from biomarker and drug discovery, the use of digital pathology is also expanding in clinical laboratories with companies such as PreciseDx offering proprietary AI algorithms that can be used to guide patient management. Their Morphology Feature Array “is unique in its ability to mine millions of data points to identify and quantify the key cellular characteristics, enabling a new level of disease characterization and therefore personalization in treatment,” said the company’s CEO Wayne Brinster.
At present, PreciseDx is in the process of releasing a commercial breast cancer test that looks for 8–12 disease-specific morphologic characteristics that are associated with current and future progression. Once they receive a digital scan or a glass slide sample they can provide clinicians with a detailed report within 2 to 3 days, which can be used to inform treatment plans.
A test for prostate cancer is now in development but the company has also used its connections with Mount Sinai, where it spun out from, to apply the technology to Parkinson’s Disease (PD), having previously had some success characterizing TAU protein in Alzheimer’s disease.
Parkinson’s disease
PreciseDx applied the Morphology Feature Array to immunohistochemistry detection of α-synuclein – a protein that is linked to PD pathogenesis – within peripheral nerves of salivary glands and showed that they could use morphology features to accurately detect peripheral Lewy type synucleinopathy (LTS), the pathological hallmark of PD, in early-stage Parkinson’s disease.
John Crary, a Professor in the Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health at the Icahn School of Medicine at Mount Sinai, said that the “industry-changing study,” published in Acta Neuropathologica Communications6, “has shown that we need to revitalize the way we think about pathology and lean into using AI to detect diseases more accurately, such as PD. This enlightens the industry to a direct case study into how computational pathology can truly advance medicine in terms of accurately identifying and detecting diseases.”
And Brinster noted that although PreciseDx’s commercial focus will remain with oncology for now, “the success with Parkinson’s and other unpublished work allows us to have a broad menu of potential diseases in which to address unmet clinical needs.”
He added: “Our mission however will always remain the same, to provide otherwise unavailable knowledge to drive better treatment decisions and patient outcomes.”
Limitations of digital pathology
Despite the advances that digital pathology can bring, there are still some limitations to the technology, particularly the wide amount of data that is needed to develop a ML model in a way that is unbiased. For example, if models are trained using biomarkers from just one cohort they may learn differences between cases and controls that are specific to that cohort, but are not actually disease-related.
Expanding the size of the training data set can also help to diminish bias that may arise, as a result of racial or ethnic differences. If the training data is not diverse it can affect the model’s performance in minority populations.
To ensure that such bias doesn’t arise, access to “consistent, compliant, and consented data is needed across points in the patient journey,” said Beck. “Partnerships with healthcare leaders, such as the one [PathAI] have with Cleveland Clinic, are crucial to help build the volume and depth of data needed across a diverse set of patient types to properly train and validate our models.”
He also noted that a lack of standardization across formats, protocols, systemic quality control and workflows within industry “can create interoperability issues and can even hinder regulatory progress” but guidelines from CAP, CLIA, the FDA, and DICOM “should help progress in this area and future novel approvals will set the additional precedent needed.”
And in spite of the limitations, Beck, like Branson, believes that now “is a transformative time in the field of pathology,” commenting that “AI will allow pathology, and specifically digital pathology, to keep pace with the rapid advancements in precision medicine and therapeutic development.”
References
1. www.fda.gov/regulatory-information/search-fda-guidance-documents/
enforcement-policy-remote-digital-pathology-devices-during-coronavirus-
disease-2019-covid-19-public
2. Mullard, A. Nature Reviews Drug Discovery 2016; 15: 447
3. www.globaldata.com/artificial-intelligence-will-disruptive-technology-
across-pharmaceutical-industry-2021-beyond/
4. Baxi, V., Edwards, R., Montalto, M., et al. Modern Pathology 2022; 35: 23–32
5. www.theguardian.com/business/2021/sep/17/gsk-teams-with-kings-college-
to-use-ai-to-fight-cancer
6. Signaevsky, M., Marami, B., Prastawa, M., et al. Acta Neuropathologica Communications 2022; 10; 21
Laura Cowen is a freelance medical journalist who has been covering healthcare news for over 10 years. Her main specialties are oncology and diabetes, but she has written about subjects ranging from cardiology to ophthalmology and is particularly interested in infectious diseases and public health.
The post How Digital Pathology is Advancing Biomarker Identification & Drug Discovery appeared first on Inside Precision Medicine.
treatment fda clinical trials dna pandemic coronavirus covid-19
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TikTok Ban Obscures Chinese Stock Gold Rush
No one wants to invest in China right now. The country’s stock market is teetering on the brink of collapse. And it is about to lose its biggest foothold…
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No one wants to invest in China right now.
The country’s stock market is teetering on the brink of collapse.
And it is about to lose its biggest foothold in America — TikTok.
Yet, beneath its crumbling economy, military weather balloons and blatant propaganda tools lie some epic opportunities…
…if you have the stomach and the knowledge.
Because as Jim Woods wrote in his newsletter last month:
“China has been so battered for so long, that there is a lot of deep value here for the ‘blood in the ‘’red’’ streets’ investors.”
And boy was he right.
However, this battle-tested veteran didn’t recommend buying individual Chinese stocks.
He was more interested in the exchange-traded funds (ETFs) like the CHIQ.
And here’s why…
Predictable Manipulation
China’s heavy-handed approach creates gaping economic inefficiencies.
When markets falter, President Xi calls on his “national team” to prop up prices.
$17 billion flowed into index-tracking funds in January as the Hang Sang fell over 13% while the CSI dropped over 7%.
Jim Woods saw this coming from a mile away.
In late February, he highlighted the Chinese ETF CHIQ in late February, which has rallied rather nicely since then.
This ETF focuses on the Chinese consumer, a recent passion project for the central government.
You see, around 2018, when President Xi decided to smother his own economy, notable shifts were already taking place.
The once burgeoning retail market had slowed markedly. Developers left cities abandoned, including weird copies of Paris (Tianducheng) and England.
Source: Shutterstock
So, Xi and co. shifted the focus to the consumer… which went terribly.
For starters, a lot of the consumer wealth was tied up in real estate.
Then you had a growing population of unemployed younger adults who didn’t have any money to spend.
Once the pandemic hit, everything collapsed.
That’s why it took China far longer to recover even a sliver of its former economy.
While it’s not the growth engine of the early 2000s, the old girl still has some life left in it.
As Jim pointed out, China’s consumer spending rebounded nicely in Q4 2023.
Source: National Bureau of Statistics of China
Combined with looser central bank policy, it was only a matter of time before Chinese stocks caught a lift.
The resurgence may be largely tied to China’s desire to travel. After all, its people have been cooped up longer than any other country.
But make no mistake, this doesn’t make China a long-term investment.
Beyond what most people understand about China’s politics, there’s a little-known fact about how they treat foreign investors.
Money in. Nothing out.
When we buy a stock, we’re taking partial ownership in that company. This entitles us to a portion of the profits (or assets).
That doesn’t happen with Chinese companies.
American depository receipts (ADRs) aren’t actual shares of a company. It’s a note that the intermediary ties to shares of the company they own overseas.
So, we can only own Chinese companies indirectly.
But there’s another key feature you probably weren’t aware of.
Many of the Chinese companies we, as Americans invest in, don’t pay dividends. In fact, a much smaller percentage of Chinese companies pay any dividends.
Alibaba is a perfect example.
Despite generating billions of dollars in cash every year, it doesn’t pay dividends.
What do its managers do with the money?
Other than squirreling away $80 billion on its balance sheets, they do share buybacks.
Plenty of investors will tell you that’s even better than dividends.
But you have no legal ownership rights in China. So, what is that ADR in reality?
We’d argue nothing but paper profits at best, and air at worst.
That’s why it’s flat-out dangerous to own shares of individual Chinese companies long-term.
Any one of them can be nationalized at any moment.
Chinese ETFs reduce that risk through diversification, similar to junk bond funds.
Short of an all-out ban, like between the United States and Russia, the majority of the ETF holdings should remain intact.
Opportunistic Investing
If China is so unstable, and capable of changing at a moment’s notice, how can investors uncover pockets of value?
As Jim showed with his ETF selection, you can have some sector or thematic idea so long as you have the data to support it.
China, like any large institution, isn’t going to change its broad economic policies overnight.
As long as you study the general movements of the government, you can steer clear of the catastrophic zones and towards the diamond caves.
Because when things look THIS bad, you know the opportunities are even juicier.
But rather than try to run this maze solo, take this opportunity to check out Jim Woods’ latest report on China.
In it, he details the broad economic themes driving the Chinese government, and how to exploit them for gain.
Click here to explore Jim Woods’ report.
The post TikTok Ban Obscures Chinese Stock Gold Rush appeared first on Stock Investor.
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The Great Escape… of UK Unemployment Reporting
https://bondvigilantes.com/wp-content/uploads/2024/03/1-the-great-escape-of-uk-unemployment-reporting-1024×576.pngThe Bank of England Monetary Policy Committee…
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The Bank of England Monetary Policy Committee potentially has a problem: it requires data to make its labour market forecasts and assessments, but the unemployment statistics have become increasingly unreliable. This is because the Labour Force Survey participation rate (on which the unemployment figures are based) has fallen below 50% since 2018 and has been as low as 15% recently[1]. What is the solution to this difficult measurement problem? An answer can be found in the classic war film, The Great Escape.
In 1943, the Escape Committee of Stalag Luft III was tasked with digging a tunnel to freedom. Unfortunately, they had a problem. They needed to measure the distance between one of the prisoner’s huts and the forest beyond the prison perimeter, but they had no reliable tools to measure this critical variable. Fortunately they had two mathematicians within the group who came up with a method to gauge the distance to the forest so that the tunnel would be long enough to ensure escape without detection. The idea was to eyeball the distance using a 20 foot tree for scale (the tree was the one ‘accurate’ measurement around which they could work with). They got individual prisoners to gauge the distance from the hut to the tree and then averaged all of the estimates. The critical distance measure was therefore the average of a large sample size of guesstimates. Fortunately, it more or less worked. Happily, modern economists have an equivalent to rely on in the area of unemployment. Their version of the Stalag Luft III tree strategy is something called the Beveridge Curve.
The Beveridge Curve is simply an observed relationship between an economy’s unemployment rate and its job vacancy rate at the same point in time. An excellent exposition can be found in the Bond Vigilantes archive[2]. When you plot the two variables against one another over a given period, the data points disclose a curve. This curve shows us that when unemployment increases, job vacancies decrease and vice versa. I have plotted the current curve below using the available data from the Office for National Statistics (ONS)[3]. The bottom left quadrant of the graph (the blue dots) relate to the Covid-19 era and the top left quadrant (the purple dots) represent the last 2 years’ worth of data. The green dots represent the remaining data from July 2004 to June 2023.
Source: Office for National Statistics, Dataset JP9Z & UNEM
Source: Office for National Statistics, Dataset JP9Z & UNEM
From these charts and new data from the ONS, we can observe that in the UK, the level of unemployment is increasing and that the job vacancy rate is decreasing. At face value, this suggests that current Bank of England monetary policy is working and that the inflation rate is slowing as the economy cools. One could argue that we are on track for a reasonably soft landing. Nothing new so far.
Things become more interesting when we consider the Beveridge Curve in conjunction with the most recent job vacancy data. We are told that there are now 814,000 job vacancies as of the 31st December 2023[4]. Ordinarily, we would use the curve and clearly be able to extrapolate from the Job Vacancy data what our Unemployment figure might be. However, we also know that the current unemployment data is unreliable, which makes this harder. Using our model inclusive of data oddities, we could extrapolate that with 814,000 job vacancies, we might expect an unemployment rate of around 3.5%. Yet, we know that our unemployment figures are unreliable so the question therefore is, how big an increase in unemployment are we likely to see given what we know about job vacancies?
In order to estimate the magnitude of the rise in unemployment, we need to look further afield. If we study the levels of economic inactivity in the UK, we can observe that they have remained stationary at 22%[5] for the last decade. We can also see that the population of the UK has risen over the same period by around 5.91%[6]. Further, we know that the Labour Force Survey (LFS) samples 40,000 households per quarter to obtain its data, but of late has had a response rate of only 15% (6,000 households). Therefore a critical question for policy makers is what is happening with the 85%, the non-responders?
Given the small sample size, it is entirely possible that the LFS suffered survey bias that is being erroneously weighted away. In other words, the LFS compensates for the paucity of response data by accessing other regional population statistics as a legitimate part of their methodology. The problems of non-responders are being addressed in upcoming LFS releases but for the time being, the data is not as clear as it ought to be. With such a small sample size, it seems possible – indeed probable – that unemployment levels are being underreported. This would explain why the current unemployment rate of 3.8%[7] is dramatically lower than the historic average of 6.7% (1971-2023). We see further evidence for this in the forecasts of the UK’s unemployment rate on Bloomberg which have been consistently above the actual levels for the last few published data points. So whilst the published headline figures might be looking reasonable, the underlying story looks like it could be hiding something more sinister.
Through it all, the Beveridge Curve remains a reasonable template. Job vacancies are definitely falling, so we should expect to see unemployment rising. Like the Stalag Luft III measurement solution, the Beveridge Curve offers a constructive way out of our present statistical dilemma. That being said, analogies can only be taken so far. Unfortunately for the inmates of Stalag Luft III, the calculation didn’t quite work and the tunnel came up short. No one actually made a Great Escape. What does this mean for UK unemployment data? Time may tell.
[1] The UK’s ‘official’ labour data is becoming a nonsense (harvard.edu)
[2] https://bondvigilantes.com/blog/2013/11/a-shifting-beveridge-curve-does-the-us-have-a-long-term-structural-unemployment-problem/
[3] Unemployment – Office for National Statistics (ons.gov.uk)
[4] https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/timeseries/jp9z/unem
[5] https://www.ethnicity-facts-figures.service.gov.uk/work-pay-and-benefits/unemployment-and-economic-inactivity/economic-inactivity/latest/#:~:text=data%20shows%20that%3A-,22%25%20of%20working%20age%20people%20in%20England%2C%20Scotland%20and%20Wales,for%20a%20job)%20in%202022
[6] https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annualmidyearpopulationestimates/mid2021
[7] https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment
unemployment covid-19 monetary policy unemployment ukInternational
Germany Is Running Out Of Money And Debt Levels Are Exploding, Finance Minister Warns
Germany Is Running Out Of Money And Debt Levels Are Exploding, Finance Minister Warns
By John Cody of Remix News
German Finance Minister…
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By John Cody of Remix News
German Finance Minister Christian Lindner is warning his own government that state finances are quickly growing out of hand, and the government needs to change course and implement austerity measures. However, the dispute over spending is only expected to escalate, with budget shortfalls causing open clashes among the three-way left-liberal coalition running the country.
With negotiations kicking off for the 2025 budget, much is at stake. However, the picture has been complicated after the country’s top court ruled that the government could not shift €60 billion in money earmarked for the coronavirus crisis to other areas of the budget, with the court noting that the move was unconstitutional.
Since then, the government has been in crisis mode, and sought to cut the budget in a number of areas, including against the country’s farmers. Those cuts already sparked mass protests, showcasing how delicate the situation remains for the government.
Lindner, whose party has taken a beating in the polls, is desperate to create some distance from his coalition partners and save his party from electoral disaster. The finance minster says the financial picture facing Germany is dire, and that the budget shortfall will only grow in the coming years if measures are not taken to rein in spending.
“In an unfavorable scenario, the increasing financing deficits lead to an increase in debt in relation to economic output to around 345 percent in the long term,” reads the Sustainability Report released by his office. “In a favorable scenario, the rate will rise to around 140 percent of gross domestic product by 2070.”
Under EU law, Germany has limited its debt levels to 60 percent of economic output, which requires dramatic savings. A huge factor is Germany’s rapidly aging population, with a debt explosion on the horizon as more and more citizens head into retirement while tax revenues shrink and the social welfare system grows — in part due to the country’s exploding immigrant population.
Lindner’s partners, the Greens and Social Democrats (SPD), are loath to cut spending further, as this will harm their electoral chances. In fact, Labor Minister Hubertus Heil is pushing for a new pension package that will add billions to the country’s debt, which remarkably, Lindner also supports.
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