X Close

UCL Department of Science, Technology, Engineering and Public Policy

Home

Applied in Focus. Global in Reach

Menu

Archive for the 'Education' Category

What can we do to decrease the cost of advanced cancer therapies and make them available for all?

By luis.lacerda, on 9 February 2024

There are 3 million people living with cancer in the UK, predicted to rise to 4 million by 2030[1]. Different societal groups are affected differently, in particular ethnic minorities who experience poorer outcomes[2]. Health inequalities are complex and their root causes diverse, including the fact that some cancers are more prevalent in specific communities[3]. Advanced research on targeted and personalised treatments can therefore bring hope to improve outcomes in the future and to “close the gap” in the access to cancer care. But how can these be made more affordable and included in holistic government strategies to manage cancer care?

Illustration of two people, two pill bottles and two DNA strandsAt UCL, the Future Targeted Healthcare Manufacturing Hub (FTHM Hub), which brings together academics, manufacturers, and policymakers, has been addressing manufacturing, business, and regulatory challenges to ensure that new targeted biological medicines can be developed quickly and manufactured at a cost affordable to society. This includes innovative research on the manufacture of promising cancer therapies ranging from Chimeric antigen receptor T-cell (CAR-T) therapies through to targeted drug therapies such as antibody-drug conjugates and cancer vaccines. The Hub engages with and supports several clinical groups at UCL that develop advanced therapy medicinal products (ATMPs), some of which have been commercialised or are being translated into the clinic.

The FTHM Hub’s work also includes more fundamental research into optimising manufacturing by innovating processes and finding new ways of reducing production costs of these therapies. Examples of this activity include manufacturing autologous CAR-T therapy at the patient’s bedside or in an automated “GMP-in-a box” system[4], which can bring about benefits in terms of cost reductions, accelerating bench-to-bedside innovation, and mitigate risks that are generated by market shortages[5].

The Hub has worked closely with healthcare specialists and regulatory authorities to analyse how CAR-Ts and other high-cost therapies affect NHS England’s ability to resource other health services. It has conducted detailed supply chain economics analysis to identify key cost of goods drivers for CAR-T therapies, supply chain optimisation, and to assess the risk-reward trade-offs between centralised and distributed manufacture.

The recent agreement reached between the Department of Health and Social Care (DHSC), NHS England and the Association of the British Pharmaceutical Industry (ABPI) on a voluntary scheme for branded medicines pricing, access and growth is a welcomed programme to explore how industry and government can better work to support the delivery of new advanced treatments for cancer, but this is not enough.

Furthermore, and for this important work to continue, investment and support on advanced manufacturing is required to understand possible implementation challenges of novel options such GMP-in-a-box in clinical settings. The new UK’s life sciences manufacturing funding to build resilience for future health emergencies is a good opportunity to do this to expand on the FTHM Hub’s work and ensure every patient living with cancer will have accessibility of treatment irrespective of geographical location.

In addition, time and cost of travel to specialised centres can pose an economic burden to patients and carers due to disparities in cancer care. New centres will also need dedicated staff to help deliver advanced therapies and the FTHM Hub is also training a new generation of professionals to enable rollout of those to patients.

In the week that marks World Cancer Day, the FTHM Hub continues to develop important work to treat patients with cancer and it is our hope at the Policy Impact Unit that we can work towards imagining new futures together, close the care gap, and bring better outcomes for all of those living with cancer.

 

References

[1] https://www.macmillan.org.uk/dfsmedia/1a6f23537f7f4519bb0cf14c45b2a629/11424-10061/Macmillan%20statistics%20fact%20sheet%20February%202023

[2] Martins, T., Abel, G., Ukoumunne, O.C. et al. Ethnic inequalities in routes to diagnosis of cancer: a population-based UK cohort study. Br J Cancer 127, 863–871 (2022). https://doi.org/10.1038/s41416-022-01847-x

[3] Delon, C., Brown, K.F., Payne, N.W.S. et al. Differences in cancer incidence by broad ethnic group in England, 2013–2017. Br J Cancer 126, 1765–1773 (2022). https://doi.org/10.1038/s41416-022-01718-5

[4] Pereira Chilima, T. & S. Farid. 2019. ‘A roadmap to successful commercialization of autologous CAR T-cell products with centralized and bedside manufacture.’ Cell Gene Therapies VI 73. Comisel, R. 2022. Decisional Tools for Supply Chain Economics of Cell and Gene Therapy Products. Diss. UCL (University College London).

[5] Bicudo, E. & I. Brass. 2023, ‘Advanced therapies, hospital exemptions & marketing authorizations: the UK’s emerging regulatory framework for point-of-care manufacture’ Cell and Gene Therapy Insights 9(1), 101-120.

Adversarial Attacks, Robustness and Generalization in Deep Reinforcement Learning

By Ezgi Korkmaz, on 20 December 2023

Reinforcement learning has achieved substantial progress on successfully completing tasks, from solving complex games to large language models (i.e. GPT-4) including many different fields from medical applications to self-driving vehicles and finance, by learning from raw high-dimensional data with the utilization of deep neural networks as function approximators.

The vulnerabilities of deep reinforcement learning policies against adversarial attacks have been demonstrated in prior studies [1,2,3,4]. However, a recent study takes these vulnerabilities one step further and introduces natural attacks (i.e. natural changes to the environment given that these changes are imperceptible) while providing a contradistinction between adversarial attacks and natural attacks. The instances of such changes include, but are not limited to creating a blur, introduction of compression artifacts, or perspective projection of the state observations at a level that humans cannot perceive the change.

Intriguingly, the results reported demonstrate that these natural attacks are at least equally, and often more imperceptible compared to adversarial attacks, while causing larger drop in policy performance. While these results carry significant concerns regarding artificial intelligence safety [5,6,7], they further raise questions on the model’s security. Note that the prior studies on adversarial attacks on deep reinforcement learning rely on the strong adversary assumption, in which the adversary has access to the policy’s perception system, training details of the policy (e.g. algorithm, neural network architecture, training dataset), and the ability to alter observations in real time with simultaneous modifications to the observation system of the policy with computationally demanding adversarial formulations. Thus, the fact that natural attacks described in [8] are black-box adversarial attacks, i.e. the adversary does not have access to the training details of the policy and the policy’s perception system to compute the adversarial perturbations, raises further questions on machine learning safety and responsible artificial intelligence.

Furthermore, the second part of the paper investigates the robustness of adversarially trained deep reinforcement learning policies (i.e. robust reinforcement learning) under natural attacks, and demonstrates that vanilla trained deep reinforcement learning policies are more robust than adversarially trained policies. While these results reveal further security concerns regarding the robust reinforcement learning algorithms, they further demonstrate that adversarially trained deep reinforcement learning policies cannot generalize at the same level as straightforward vanilla trained deep reinforcement learning algorithms.

This study overall, while providing a contradistinction between adversarial attacks and natural black-box attacks, further reveals the connection between generalization in reinforcement learning and the adversarial perspective.

Author’s Note: This blog post is based on the paper ‘Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness’ published in AAAI 2023.
References:
[1] Adversarial Attacks on Neural Network Policies, ICLR 2017.
[2] Investigating Vulnerabilities of Deep Neural Policies. Conference on Uncertainty in Artificial Intelligence (UAI).
[3] Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs. AAAI Conference on Artificial Intelligence, 2022. [Paper Link]
[4] Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions. International Conference on Machine Learning, ICML 2023. [Paper Link]
[5] New York Times. Global Leaders Warn A.I. Could Cause ‘Catastrophic’ Harm, November 2023.
[6] The Washington Post. 17 fatalities, 736 crashes: The shocking toll of Tesla’s Autopilot, June 2023.
[7] The Guardian. UK, US, EU and China sign declaration of AI’s ‘catastrophic’ danger, November 2023.
[8] Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness, AAAI Conference on Artificial Intelligence, 2023. [Paper Link]

In the slums of Kampala, the phrase “survival for the fittest” takes on a whole new meaning: reflections from a recent field trip studying electricity access in Nakulabye slum, Kampala, Uganda

By penlope.yaguma.20, on 31 October 2022

By Penlope Yaguma

Penlope Yaguma is a 3rd year PhD student of Energy and Development Policy at the UCL Department of Science, Technology, Engineering and Public Policy (UCL STEaPP) and the UCL Engineering for International Development Centre (EfID). Her broad research interests are on electricity access in slums and informal settlements in African cities, with a specific focus on Uganda’s cities. Penlope’s work is inspired by her own experiences of growing up and living in Uganda, and she hopes to bring her formal training in electrical engineering and sustainable energy systems to understanding and creating solutions for the inequalities and injustices in service delivery and infrastructure provision in African cities.

Everyone looks down on us because we live in the ghetto, but deep down they know that these ghettos are the heartbeat of Kampala.

Despite slums’ close proximity to grid infrastructure or sometimes literally under the grid, accessing electricity in Kampala’s slums remains precarious, costly or downright unsafe

How it all began: In September 2022, I set out to do the fieldwork and field data collection for my PhD research in Nakulabye slum, one of over 60 slum settlements in Uganda’s capital Kampala. The plan was to conduct household surveys, hold focus group discussions in the settlement and interview key stakeholders on all matters electricity access specifically and access to social services and infrastructure more broadly. I was very fortunate to work with a passionate field team of geography students from Makerere University’s Urban Action Lab and the Centre for Climate Change Research and Innovation, and community guides who were residents of the settlement. We also received overwhelming support and assistance from the local council leaders (LC1s) of all nine administrative villages/zones that make up Nakulabye settlement. Many generously shared their experiences of securing social services for their jurisdictions and improving livelihoods for community members, families, and businesses. The devastating effects of Covid-19 and increasing cost of living are still being felt in Nakulabye, forcing some to close their businesses or pack up their families and move back to the village. Following two settlement walks, training and piloting the survey questionnaire, the actual data collection began – lasting about 2 weeks in total. In this blog post, I reflect upon this fieldwork exercise and write about my experiences and key observations.

(more…)

The wicked fuel subsidies and the complexity of their reforms: Lessons from Indonesia

By Muhamad Rosyid Jazuli, on 15 March 2022

Climate change has been a hot issue for most countries for several decades. Experts have expressed significant concern about the overconsumption of fuels across the globe. Its main driver: fuel subsidies.

Our latest publication (Jazuli, Steenmans, and Mulugetta 2021) highlights the importance of reducing global fuel subsidies. Nevertheless, studies are incredulous how these subventions persist. Our review shows that subsidy reforms are not just a matter of cuts to these subventions and the subsequent fuel price increase. It is more complex than that.

Photo by CEphoto, Uwe Aranas

Globally, in 2014, fuel consumption subsidies from various countries accounted for 13% of global GHG emissions (IEA, 2015). Fuel subsidies also often lead to carbon lock-in where development cannot be separated from fuel even though renewable energy potential is abundant (Seto et al., 2016).

Fuel subsidies can reduce logistics and transportation costs to suppress prices. However, these policies often come with a variety of ramifications. In addition to exacerbating global warming, fuel subsidies are hampering investment in fundamental sectors such as education, health, and renewable energy. In addition, these subsidies spoil the rich rather than help the poor. In Indonesia, for example, more than 80% of these subsidies are enjoyed by the richest 50% (Diop, 2014).

(more…)

PhD Episode II: The Return of Ethnographic Methods

By laurent.liote.19, on 16 December 2021

Hi there, it’s been a while! I guess I’ve made some progress since the last time I wrote a post like this one. Rest assured the aim of my PhD has not changed, I’m still focused on understanding how engineering advice and related modelling insights are deployed in energy policy practice (the origin story can be found here). This post is about the initial work I’ve done to answer this question and where I’m going next.

In a UK government department not so far away…

So, what have I been up to in the last year then? Well, I did an initial case study with an engineering advice team within the UK government that provides advice on energy policy questions to the rest of their department. I interviewed engineers and policy advisors working together to gain insight into ‘the engineering-policy interface’ (a fancy way of saying ‘how engineers and policy advisors interact’). I turned the themes that emerged from the interviews into academic database search terms which returned four different strands of literature: science advice, engineering and philosophy, expertise in policy and models as boundary objects. I carried out a review of these fields and compared the literature’s conclusions against my findings, I call that ‘PhD Episode I’.

And what did I make of Episode I then? Like a first episode in what I hope to be a trilogy, it was interesting, set up the characters and storyline nicely but left quite a few questions unanswered. From what I saw, most of the engineering advice consisted of explaining a technology in layman’s terms to policy analysts, answering a question by providing a summary/diagram or designing/running a model. But that’s just scratching the surface and several findings warrant further investigation, constituting the basis for my second case study: Episode II.

(more…)