Background: The majority of colorectal cancer cases are diagnosed following symptomatic presentation in the United Kingdom. Aim: To identify windows of opportunity for timely investigations or referrals in patients presenting with colon and rectal cancer-relevant symptoms or abnormal blood tests. Design and setting: Retrospective cohort study using linked primary care and cancer registry data of colorectal cancer patients diagnosed in England between 2012-2015. Methods: Monthly consultation rates for relevant clinical features (change in bowel habit, rectal bleeding, abdominal pain, mass, constitutional symptoms, and other bowel symptoms) and abnormal blood test results (low haemoglobin, high platelets and inflammatory markers) up to 24 months pre-diagnosis were calculated. Poisson regression adjusted for age, sex and relevant comorbidities was used to estimate the most likely month when consultation rates increased above baseline trend. Results: 5033 colon and 2516 rectal cancer patients were included. Consultations for all examined clinical features and abnormal blood tests increased in the year pre-diagnosis. Rectal bleeding was the earliest clinical feature to increase from baseline rate: 10 months (95%CI 8.3-11.7) pre-diagnosis for colon cancer; 8 months (95%CI 6.1-9.9) for rectal cancer. Low haemoglobin, high platelets and inflammatory markers increased from as early as 9 months pre-diagnosis. Conclusion: Our study found evidence for early increase in rates of consultation for relevant clinical features and abnormal blood tests in patients with colorectal cancer, suggesting that earlier instigation of cancer-specific investigations or referrals may be warranted in some symptomatic patients.

The gold‐standard approach for modeling pharmacokinetic mediated drug–drug interactions is the use of physiologically‐based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug‐specific data generated from a wide variety of in vitro and in vivo models, which are later refined with clinical data and system‐specific parameters. Machine learning has the potential to be utilized for the prediction of drug–drug interactions much earlier in the drug discovery cycle, using inputs derived from, among others, chemical structure. This could lead to refined chemical designs in early drug discovery. Machine‐learning models have many advantages, such as the capacity to automate learning (increasing the speed and scalability of predictions), improved generalizability by learning from multicase historical data, and highlighting statistical and potentially clinically significant relationships between input variables. In contrast, the routinely used mechanistic models (physiologically‐based pharmacokinetic models and population pharmacokinetics) are currently considered more interpretable, reliable, and require a smaller sample size of data, although insights differ on a case‐by‐case basis. Therefore, they may be appropriate for later stages of drug–drug interaction assessment when more in vivo and clinical data are available. A combined approach of using mechanistic models to highlight features that can be used for training machine‐learning models may also be exploitable in the future to improve the performance of machine learning. In this review, we provide concepts, strategic considerations, and compare machine learning to mechanistic modeling for drug–drug interaction risk assessment across the stages of drug discovery and development.

Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug–drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine‐learning models have been developed that can classify drug–drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug–drug interaction, regression‐based machine learning should be explored. Therefore, this study investigated the use of regression‐based machine learning to predict changes in drug exposure caused by pharmacokinetic drug–drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug–drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression‐based supervised machine‐learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross‐validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression‐based machine‐learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug–drug interaction risk assessment for new drug candidates.

Increasing clinical data on sex-related differences in drug efficacy and toxicity has highlighted the importance of understanding the impact of sex on drug pharmacokinetics and pharmacodynamics. Intrinsic differences between males and females, such as different CYP enzyme activity, drug transporter expression or levels of sex hormones can all contribute to different responses to medications. However, most studies do not include sex-specific investigations, leading to lack of sex-disaggregated pharmacokinetic and pharmacodynamic data. Based available literature, the potential influence of sex on exposure-response relationship has not been fully explored for many drugs used in clinical practice, though population-based pharmacokinetic/pharmacodynamic modelling is well-placed to explore this effect. The aim of this review is to highlight existing knowledge gaps regarding the effect of sex on clinical outcomes, thereby proposing future research direction for the drugs with significant sex differences. Based on evaluated drugs encompassing all therapeutic areas, 25 drugs demonstrated a clinically meaningful sex differences in drug exposure (characterised by ≥ 50% change in drug exposure) and this altered PK was correlated with differential response.