Algorithmic causal effect identification

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Jordi Vitrià i Marca i Álvaro Parafita Martínez[en] Our evolution as a species made a huge step forward when we understood the relationships between causes and effects. These associations may be trivial for some events, but they are not in complex scenarios. To rigorously prove that some occurrences are caused by others, causal theory and causal inference were formalized, introducing the do-operator and its associated rules. The main goal of this project is to understand and implement in Python some algorithms to compute conditional and non-conditional causal queries from observational data. To this end, we first present some basic background knowledge on probability and graph theory, before introducing important results on causal theory, used in the construction of the algorithms. We then thoroughly study the identification algorithms presented by Shpitser and Pearl in 2006 [SP 2006a, SP 2006b], explaining our implementation in Python alongside. The main identification algorithm can be seen as a repeated application of the rules of do-calculus, and it eventually either returns an expression for the causal query from experimental probabilities or fails to identify the causal effect, in which case the effect is nonidentifiable. We introduce our newly developed Python library and give some usage examples towards the end of the dissertation

TOWARDS A HOLISTIC RISK MODEL FOR SAFEGUARDING THE PHARMACEUTICAL SUPPLY CHAIN: CAPTURING THE HUMAN-INDUCED RISK TO DRUG QUALITY

Counterfeit, adulterated, and misbranded medicines in the pharmaceutical supply chain (PSC) are a critical problem. Regulators charged with safeguarding the supply chain are facing shrinking resources for inspections while concurrently facing increasing demands posed by new drug products being manufactured at more sites in the US and abroad. To mitigate risk, the University of Kentucky (UK) Central Pharmacy Drug Quality Study (DQS) tests injectable drugs dispensed within the UK hospital. Using FT-NIR spectrometry coupled with machine learning techniques the team identifies and flags potentially contaminated drugs for further testing and possible removal from the pharmacy. Teams like the DQS are always working with limited equipment, time, and staffing resources. Scanning every vial immediately before use is infeasible and drugs must be prioritized for analysis. A risk scoring system coupled with batch sampling techniques is currently used in the DQS. However, a risk scoring system only allows the team to know about the risks to the PSC today. It doesn’t let us predict what the risks will be in the future. To begin bridging this gap in predictive modeling capabilities the authors assert that models must incorporate the human element. A sister project to the DQS, the Drug Quality Game (DGC), enables humans and all of their unpredictability to be inserted into a virtual PSC. The DQG approach was adopted as a means of capturing human creativity, imagination, and problem-solving skills. Current methods of prioritizing drug scans rely heavily on drug cost, sole-source status, warning letters, equipment and material specifications. However, humans, not machines, commit fraud. Given that even one defective drug product could have catastrophic consequences this project will improve risk-based modeling by equipping future models to identify and incorporate human-induced risks, expanding the overall landscape of risk-based modeling. This exploratory study tested the following hypotheses (1) a useful game system able to simulate real-life humans and their actions in a pharmaceutical manufacturing process can be designed and deployed, (2) there are variables in the game that are predictive of human-induced risks to the PSC, and (3) the game can identify ways in which bad actors can “game the system” (GTS) to produce counterfeit, adulterated, and misbranded drugs. A commercial-off-the-shelf (COTS) game, BigPharma, was used as the basis of a game system able to simulate the human subjects and their actions in a pharmaceutical manufacturing process. BigPharma was selected as it provides a low-cost, time-efficient virtual environment that captures the major elements of a pharmaceutical business- research, marketing, and manufacturing/processing. Running Big Pharma with a Python shell enables researchers to implement specific GxP-related tasks (Good x Practice, where x=Manufacturing, Clinical, Research, etc.) not provided in the COTS BigPharma game. Results from players\u27 interaction with the Python shell/Big Pharma environment suggest that the game can identify both variables predictive of human-induced risks to the PSC and ways in which bad actors may GTS. For example, company profitability emerged as one variable predictive of successful GTS. Player\u27s unethical in-game techniques matched well with observations seen within the DQS

COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19

The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet (http://compmedchem.unibo.it/covidrugnet), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies

Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.Comment: 45 pages, 6 figures, 7 table

A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally, this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, each being present at a pre-determined concentration, thereby determining if the sample isresistant or susceptible to each sample. The minimum inhibitory concentration (MIC) of a drug is the lowestconcentration that inhibits growth and is a more useful quantity but requires each sample to be tested at a range ofconcentrations for each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is still an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11–17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing

A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally, this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, each being present at a pre-determined concentration, thereby determining if the sample isresistant or susceptible to each sample. The minimum inhibitory concentration (MIC) of a drug is the lowestconcentration that inhibits growth and is a more useful quantity but requires each sample to be tested at a range ofconcentrations for each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is still an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11-17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing

Drug Reviews: Cross-condition and Cross-source Analysis by Review Quantification Using Regional CNN-LSTM Models

Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are known to perform better than usual machine learning models in the case of textual data sequences. Second, how effective is it to migrate such information extraction models across different drug review data sets and across different disease conditions. Therefore three experiments were designed, first, an In-domain experiment where train and test data are from the same dataset. Two more experiments were conducted to examine the migration capability of models, namely cross-data source, where train and test are from different sources and cross-disease condition model training, where train and test data belong to different disease conditions in the same dataset. The experiments were evaluated using popular metrics such as RMSE, MAE, R2 and Pearson’s coefficient and the results showed that the proposed deep learning regression model works less successfully when compared to the machine learning sentiment extraction models in the literature, which were done on the same datasets. But, this study contributes to the existing literature in the quantity of research work done and in quality of the model and also suggests the future researchers on how to improve. This work also addressed the shortcomings in the literature by introducin