In the use case example presented here for illustrative purposes, the methodology outlined in Sect. 3.7 was followed for each component in order to assess the potential change in risks when marketed product A, a small molecule that inhibits three tyrosine kinase receptors is used in combination with marketed product B, an antibody that blocks an immune checkpoint inhibitor. The example presented below shows an evaluation of the combination as a whole (e.g., SOC-level risk overlap, target expression, MOA, and signaling), and then narrows down the evaluation to focus on some of the risks of toxicity within a single SOC. The same process was conducted for every risk for the combination. This use case example did not use data that would only be available within a company developing those IMPs. However, where available, company-proprietary data should be used to evaluate company IMPs as part of the combination risk profiling, for example, from investigator brochures, toxicology reports, and preclinical, translational, and clinical data.

3.1 Risks (PT and SOC; ADR and PR)

Preclinical and clinical data from the published literature were reviewed for products A and B, including the product labels, for example, Summary of Product Characteristics and US Prescribing Information. While overlapping ADRs were identified within multiple SOCs for the combination assessed herein (Fig. 2a), for illustrative purposes, the analysis presented in this article focused only on evaluation of PRs and identified risks within the Gastr SOC for each product.

Fig. 2

Venn diagrams of overlapping and non-overlapping identified and potential risks. a Risks at the System Organ Class (SOC) level. b Risks within the Gastrointestinal disorders (Gastr) SOC. Risks are presented at the Preferred Term (PT) level. *potential risk for product A, #potential risk for product B, §hyper-sensitivity (Immune system SOC), AE adverse event, DDI drug–drug interaction. SOCs [Blood: Blood and lymphatic system disorders, Card: Cardiac disorders, Cong: Congenital, familial and genetic disorders, Endo: Endocrine disorders, Ear: Ear and labyrinth disorders, Eye: Eye disorders, Gnrl: General disorders and administration site conditions, Hepatobil: Hepatobiliary disorders, Immun: Immune system disorders, Inj&P: Injury, poisoning and procedural complications, Investig: Investigations, Metabol: Metabolism and nutrition disorders, Musc: Musculoskeletal and connective tissue disorders, Nerv: Nervous system disorders, Preg: Pregnancy, puerperium and perinatal conditions, Renal: Renal and urinary disorders, Repro: Reproductive system and breast disorders, Resp: Respiratory, thoracic and mediastinal disorders, Skin: Skin and subcutaneous tissue disorders, Vasc: Vascular disorders]

The ADR and PRs of similar class products and those with same MOA were also taken into consideration for identification of overlapping and non-overlapping risks at SOC and/or Preferred Term (PT) level. In this particular example within the Gastr SOC, the PTs of abdominal pain, nausea, vomiting, constipation, and diarrhea were identified as overlapping ADRs for products A and B (Fig. 2b).

In addition, an evaluation of GI toxicity for the same class of drugs for both products suggested there may also be a potential for an overlap of risks, which could be characterized by PTs of abdominal discomfort, abdominal distension, abdominal pain lower, intestinal perforation, GI leak, and perforation. The overlap in ADRs potentially suggests that an increased incidence or severity of the specific ADR may result when product A and B are used in combination, for example, if diarrhea has a frequency of common (≥ 1/100 to < 1/10) for product A and rare or uncommon (≥ 1/1000 to < 1/100) for product B, then the combination of the two products may result in a frequency of common (≥ 1/100 to < 1/10) or very common (≥ 1/10) per CIOMS Working Group III classifications [14]. However, as indicated in the methodology, all components must be taken in totality for a more accurate prediction of combination effects. To this end, these overlapping ADRs and overlapping PRs identified at SOC and PT levels, including those for similar-class products, were entered into the risk (ADR and PR) and class-effect sections of the prediction table (Sect. 3.7).

3.2 Target Expression

Protein and messenger RNA (mRNA) expression of the targets of each product in the combination were evaluated. As product A is known to act on three tyrosine kinase receptors, analyses of the target expression for all three receptors plus the immune checkpoint inhibitor receptor for product B were conducted. Expression of the four targets in both normal and diseased tissue was considered. Information on overlapping and non-overlapping cell, tissue, and organ expression for all four targets was gathered from the published literature, the product labels, and the public domain databases. In this example, overlapping expression of the four targets was noted in the GI tract including the colon, and also in male tissues, female tissues, muscle tissues, adipose and soft tissue, bone marrow, and lymphoid tissue (Fig. 3a).

Fig. 3

Overlapping expression of the targets of product A (R1 [blue], R2 [yellow], and R3 [green]) and product B (X, [red]) by organ and tissue type. Top panel shows relative expression per organ. Bottom panel shows combined relative cellular expression collectively per tissue type. a Relative expression of messenger RNA (mRNA) and protein at the organ level. Each bar represents the highest expression score found in a particular group of tissues. Relative expression has been adapted using data from Human Protein Atlas version 21.0 (HPA, https://www.proteinatlas.org) and the Genome Tissue Expression (GXTe) project (https://www.gtexportal.org/home/), 15-16). Relative: mRNA expression summary shows the consensus data based on normalized expression (nTPM) values. Relative protein expression levels are based on a best estimate of the true protein expression. Data were combined from the following figures (accessed 24 May 2022): https://www.proteinatlas.org/ENSG00000120217-CD274/tissue, https://www.proteinatlas.org/ENSG00000102755-FLT1/tissue, https://www.proteinatlas.org/ENSG00000128052-KDR/tissue, https://www.proteinatlas.org/ENSG00000037280-FLT4/tissue and shared under license https://creativecommons.org/licenses/by/4.0/ and https://www.proteinatlas.org/about/licence. b Relative single cell RNA expression at the cellular level for individual cell types grouped within a specific tissue type. Relative single-cell RNA expression has been adapted using data from Human Protein Atlas version 21.0 (accessed 24 May 2022): (https://www.proteinatlas.org, 17-18). Single-cell type clusters were normalized separately from other transcriptomics datasets using Trimmed mean of M values (TMM). To generate expression values per cell type, clusters were aggregated per cell type by first calculating the mean normalized expression (nTPM) in all cells with the same cluster annotation within a dataset. The values for the same cell types in different data sets were then mean averaged to a single aggregated value. Data were combined from the following sources: https://www.proteinatlas.org/ENSG00000120217-CD274/single+cell+type, https://www.proteinatlas.org/ENSG00000102755-FLT1/single+cell+type, https://www.proteinatlas.org/ENSG00000128052-KDR/single+cell+type, https://www.proteinatlas.org/ENSG00000037280-FLT4/single+cell+type and shared under license https://creativecommons.org/licenses/by/4.0/ and https://www.proteinatlas.org/about/licence

At the cellular level, overlapping expression was found in single cells within endocrine, epithelial, muscle, neuronal, epithelial, and trophoblast tissues (grouped by tissue in Fig. 3b, and per individual cell type in Fig. 4a), and cells of the immune system including T cells, B cells, natural killer cells, monocytes, and neutrophils (Fig. 4b). These data were entered into the prediction table (Sect 3.7). As the targets of both products are expressed in the GI tract, and immune cells, which also express the four targets are detectable in the GI tract, particularly in certain disease indications such as gastric cancer, engagement of these targets by products A and B may also contribute to a change in the frequency or severity of Gastr SOC-related PTs.

Fig. 4

Overlapping expression of the targets of product A (R1 [blue], R2 [yellow] and R3 [green]) and product B (X, [red]) for individual cells. Top panel shows relative expression per cell type from multiple tissues. Bottom panel shows combined relative cellular expression per cell type for immune cells. a Relative single cell RNA expression at the cellular level for individual cells. Relative single cell RNA expression has been adapted using data from Human Protein Atlas v21.0 (accessed on 24th May 2022): (https://www.proteinatlas.org, 17–18). Single cell type clusters were normalized separately from other transcriptomics datasets using Trimmed mean of M values (TMM). To generate expression values per cell type, clusters were aggregated per cell type by first calculating the mean normalized expression (nTPM) in all cells with the same cluster annotation within a dataset. The values for the same cell types in different data sets were then mean averaged to a single aggregated value. Data was combined from the following sources: https://www.proteinatlas.org/ENSG00000120217-CD274/single+cell+type, https://www.proteinatlas.org/ENSG00000102755-FLT1/single+cell+type, https://www.proteinatlas.org/ENSG00000128052-KDR/single+cell+type, https://www.proteinatlas.org/ENSG00000037280-FLT4/single+cell+type and shared under license https://creativecommons.org/licenses/by/4.0/ and https://www.proteinatlas.org/about/licence. Top panel shows relative expression per cell type. Bottom panel shows combined relative cellular expression collectively per tissue type. b Relative single cell RNA expression in immune cells. Relative single cell RNA expression in immune cells has been adapted using data from Human Protein Atlas (HPA, https://www.proteinatlas.org) from the Monaco dataset (19) that contains data contains data for 29 immune cell types within the peripheral blood mononuclear cell (PBMC) fraction of healthy donors using RNA-seq and flow cytometry (18–19). TPM gene expression values of all samples within each data source were normalized separately using Trimmed mean of M values (TMM) to allow for between-sample comparisons. The resulting normalized transcript expression values, denoted nTPM, were calculated for each gene in every sample. Data was combined from the following sources (accessed on 24th May 2022): https://www.proteinatlas.org/ENSG00000120217-CD274/immune+cell, https://www.proteinatlas.org/ENSG00000102755-FLT1/immune+cell, https://www.proteinatlas.org/ENSG00000128052-KDR/immune+cell, https://www.proteinatlas.org/ENSG00000037280-FLT4/immune+cell and shared under license https://creativecommons.org/licenses/by/4.0/ and https://www.proteinatlas.org/about/licence

3.3 Drug MOA and Target Biology

The primary MOA and downstream effects of the products and their targets were evaluated using information gathered from the literature, including product labels. For both products, the effect of the drugs binding to their targets was considered together with the biological function of the target, target binding partners, ligands or substrates, and the resultant inhibition or activation of relevant pathways triggered by their receptors due to the MOA of the drug and biology of target for each product in the combination (three receptors for product A plus 1 receptor for product B). For example, product A binds to three tyrosine kinase receptors thereby blocking binding of the relevant ligands or substrates of the receptors. Therefore, the biology of the three receptors, and in this case, inhibition of the biological activity that usually results from each ligand/substrate–receptor interaction, and the biological effect of accumulating free substrate (that could be likened to supplementation or over-expression of the substrate) was also considered in the analysis. In addition, for the combination partner, binding of drug product B to its immune checkpoint inhibitor receptor results in blocking of the ligand–receptor interaction that induces immune suppression, resulting in removal of the immunological brakes to allow activation of immune signaling pathways and processes such as cellular cytotoxicity, immune cell activation, inflammation, and cytokine induction (Fig. 5a, b).

Fig. 5

Mechanism of action of product A, an inhibitor of three tyrosine kinase receptors, and product B, a checkpoint inhibitor. a The interaction between the effects of product A and product B leading to potentially compounded adverse events (AEs). b Inhibition of target biology by product A and B. This figure has been adapted from Ott et al. [20] under license https://creativecommons.org/licenses/by/4.0/. The figure has been modified to (i) show a generalization of function of tyrosine kinase receptors, (ii) include overlapping activity of checkpoint inhibitors, and (iii) indicate functions that may be blocked by products A and/or B. Dotted lines indicate differentiation from iMC to TAM and iDC. AE adverse event, B B-cell, Blood Blood and lymphatic system disorders, Card Cardiac disorders, CLS Capillary leak syndrome, CTL Cytotoxic T cell, DAMP damage-associated molecular patterns, DC dendritic cell, Endo Endocrine disorders, Gastr Gastrointestinal disorders, Hepatobil Hepatobiliary disorders, iDC immature dendritic cell, IDO indolamine 2-3-dixoygenase, IFN-γ interferon gamma, IL-10 interleukin-10, iMC immature myeloid cell, macs macrophages, matDC mature dendritic cell, MDSC myeloid-derived suppressor cell, Musc Musculoskeletal and connective tissue disorders, Nerv Nervous system disorders, neut neutrophils, NFκB nuclear factor kappa-light-chain-enhancer of activated B cells, NK natural killer cell, NLR nucleotide-binding oligomerization domain (NOD)-like receptors, PAMP pathogen associated molecular pattern, PRR pattern recognition receptors, PT Preferred Term, Renal Renal and urinary disorders, Resp respiratory, R1-3 receptor 1-3, ROS reactive oxygen species, Skin Skin and subcutaneous tissue disorders, SOC System Organ Class, TAM tumor-associated macrophage, TGF-β transforming growth factor-beta, TLR Toll-like receptor, tox toxicity, T-reg T-regulatory cell, Vasc Vascular disorders

The potential overlap of biological processes activated or suppressed by the actions of products A and B, their downstream effects, and the potential effect of one on the other and how they may interact in combination were also considered. In this example, based on the collective data from the literature and product label, blocking of the function of the three tyrosine kinase receptors by chemical, biological, or genetic intervention (gene knockout or mutation), the inhibitory action of product A on its three target receptors could potentially have resulted in endothelial cell dysfunction; inhibition of angiogenesis, vasculogenesis, vascular permeability and vasodilation; embryofetal malformation, immune activation, immune cell migration and chemotaxis, and activation of complement and nuclear factor-kappa-light-chain-enhancer of activated B cells. Similarly, the action of product B (or chemical, biological, or genetic intervention) could result in acute or chronic inflammation. The overlap between the actions of product A and B could therefore potentially result in enhancement of immune-mediated toxicity. As had been noted in the analysis of target expression, as the cells and tissues that express the targets of both products include immune cells and endothelial cells in the GI tract, the overlapping MOA and drug target biology could also contribute to enhancement of GI toxicity.

3.4 Target Signaling Pathways

In the next stage of the analysis, we took a deeper dive into the biology of the targets and the effects of both drug products, by evaluating signaling pathways for the target(s) of each product in the combination (three tyrosine kinase receptors and an immune checkpoint inhibitor receptor), focusing on signaling pathways that have been reported to lead to, or be linked with, specific AEs. We analyzed the intersection of pathways of these targets in combination based on information from the literature, and databases and tools including Biocyc, cBioPortal Cytoscape, KEGG, and GeneCards. Overlapping and converging signaling pathways were identified for immune-mediated AEs. For the combination of product A with product B, we identified an overlap in signaling pathways via nuclear factor-kappa-light-chain-enhancer of activated B cells and PI3K that could lead to immune modulation, and HIF1-α that could lead to angiogenesis (Fig. 6); therefore, the blocking of these pathways by product A plus product B could potentially contribute to enhanced immune activation and anti-angiogenic function, and resultant AEs. These findings supplement and substantiate the information obtained thus far that the combination of products A and B may lead to enhancement of GI-related toxicities.

Fig. 6

High-level signaling overlay for product A and product B, showing common and separate or different signaling pathways and their biological effects. Akt protein kinase B, CPI-L checkpoint inhibitor ligand, CPI-R checkpoint inhibitor receptor, ERK extracellular signal-regulated kinases, HIF-1α hypoxia-inducible factor α, MAPK mitogen-activated protein kinases, MEK mitogen-activated ERK kinase, NFκB nuclear factor kappa-light-chain-enhancer of activated B cells, p38 p38 mitogen-activated protein kinases, PKC protein kinase C, PI3K phosphatidylinositol 3-kinase, RAF rapidly accelerated fibrosarcoma, Ras rat sarcoma virus, ROS reactive oxygen species, *PI3K/Akt/Ras-MEK/ERK

3.5 Pharmacology

For this use case analysis of these two marketed products, we conducted an analysis of the pharmacology including the pharmacokinetics and pharmacodynamics of each product in the combination, using published preclinical and clinical data from the literature and product labels, and public domain data available through tools including Biocyc and OFF-X. Parameters evaluated were route of administration, route of clearance, drug accumulation, drug metabolism, enzyme involvement, drug biodistribution, potential for drug–drug interactions, secondary pharmacology, off-target effects, and downstream effects of product A and product B. We determined that there was no overlap in pharmacology or potential for drug–drug interactions between the two products. Product A is administered orally, therefore exposing the GI tract to the product, whereas product B is administered by an intravenous infusion. Product A is metabolized in the liver via cytochrome P450 (CYP) 3A4/5 and to a lesser extent by CYP1A2, CYP2C19, and UGT1A1 enzymes, and metabolites could be recovered in feces and urine, whereas the primary mechanism of product B elimination is proteolytic degradation. The aqueous solubility of product A is pH dependent, with a higher pH resulting in lower solubility. In addition, in vitro studies suggested the potential for product A to inhibit the activity of CYP1A2 and CYP2C8 and have off-target effects of inhibition of the efflux transporter, P-glycoprotein. However, because the metabolism and pharmacology of product B are not dependent on any of these factors, and product B has not been reported to alter pH, we did not identify any direct pharmacological effects of either product on the other. Therefore, we could eliminate the potential for pharmacology to alter GI-related toxicity in this combination.

3.6 Interaction of Non-Overlapping Components

Having completed the analysis of the individual components of this methodology framework and identified potential areas of overlap that may lead to altered GI toxicity, the next part of the methodology framework we undertook was to evaluate the potential impact or interaction of non-overlapping components on the frequency, severity, and TTO of AEs for the combination of products A and B. For this analysis, we evaluated:

non-overlapping AEs and the potential for secondary AEs (downstream or subsequent AEs) occurring as a result of the first AE (e.g., electrolyte disturbance following diarrhea and vomiting or bleeding following thrombocytopenia);

expression of targets for product A and B on different cells, but within the same tissue or organ (e.g., immune cells and endothelial cells within the same tissue);

an AE or MOA for one product in an area where the second product is expressed;

non-overlapping but potentially interacting pharmacological characteristics of one product with another or with other components for the second product (e.g., accumulation of one product in a compartment where the other product has toxic effects).

With respect to GI-related toxicities, areas where non-overlapping components for both products may have predicted an enhancement of toxicity were identified as follows: product A is delivered orally and absorbed through the GI tract. Product A has an ADR of diarrhea and product B has an ADR of immune-mediated colitis. Both are GI related although the PTs are not overlapping. However, the net effect may be compounded because symptoms of colitis often manifest as abdominal pain, nausea, vomiting, and diarrhea. The targets of product A are expressed in the GI tract and the targets of both products are expressed on immune cells. Products A and B both induce immune activation and inflammation but to a different extent. The presence of activated immune cells in the GI tract may lead to enhanced GI toxicity.

3.7 Analysis Summary and Prediction Outcome

Results from each component analysis were entered in Fig. 7. Taking into consideration the overlapping and non-overlapping components that could alter the risk of GI toxicity, using this methodology we were able to predict that the PTs diarrhea, nausea, vomiting, and abdominal pain would be enhanced in the combination of the products compared with the monotherapies in clinical trials. We used an arbitrary cut-off value of a 5 percentage point difference to indicate an increase over the highest reported percentage of that ADR for one of the products.

Fig. 7

Prediction table example for select Preferred Terms (PTs) within the Gastrointestinal disorders (Gastr) System Organ Class (SOC). ADR adverse drug reaction, AE adverse event, DDI drug–drug interaction, GI gastrointestinal, imAE immune-mediated adverse events, MOA mechanism of action, mRNA messenger ribonucleic acid, NFκB nuclear factor kappa-light-chain-enhancer of activated B cells, PD pharmacodynamic, PI3K phosphatidylinositol 3-kinase, PK pharmacokinetic

For proof of concept and validation of our methodology framework, we reviewed the results from the clinical trials in monotherapy and in combination presented in the product labels for two marketed products of the classes described above in our use case example: product A [21] and B [22]. We found that the incidence of the ADR PTs of diarrhea, nausea, and abdominal pain were indeed increased in combination above those of either or both of the individual products used as monotherapy, thereby validating our combination risk assessment methodology for prediction of altered toxicity. For example, while the incidence of all-grade diarrhea was 55% for product A and 23% for product B, the combination resulted in a 62% incidence of all-grade diarrhea. Similarly, abdominal pain increased from 14% for product A and 16% for product B to 22% for the combination. In other instances, the incidence of a particular PT was increased in the combination compared with that of just one of the monotherapies, highlighting the contribution of the combination partner to the toxicity. For example, while the incidence of all-grade nausea was 32% for product A and 22% for product B, when used in combination the incidence was 34% for all-grade nausea. This is an increase in incidence for the combination compared with that of product B alone but not product A alone.

Following the above methodology, a similar prediction was made for enhanced toxicity for PTs within nine SOCs and a prediction of no change in the frequency or severity of risks within three SOCs (Table 6). We predicted that the incidence of all-grade events for the PTs of anemia, thrombocytopenia, decreased appetite, and rash in the SOCs of Blood, Metabolism and Skin would likely not be higher with the combination compared to the monotherapies. Where data were available, the combination study data [21, 22] confirmed this prediction to be accurate for these PTs (anemia [product A: 35%, product B: 35%, combination: 21%], thrombocytopenia [A: 15%, B: 27%, combination: 27%], decreased appetite [A: 34%, B: 20%, combination: 26% all grade], and rash [A: 13%, B: 22%, combination: 26%]; data were incomplete for the other three PTs in the Skin SOC). We also predicted that PTs within the Investigations SOC and Metabolism and Nutrient Disorders SOC might be increased.

Table 6 Prediction and validation of prediction example for select PTs within all SOCs tested

As indicated in Table 6, results were available for some, but not all of the PTs in the combination study (shown as N/A), and some predictions were partially accurate at the SOC level as not all PTs were increased, for example, within the Investigations SOC (“Investig”), we predicted the frequency of the following PTs (weight decreased, amylase increased, aspartate aminotransferase increased, alanine aminotransferase increased, alkaline phosphatase increased, blood creatinine increased, lipase increased, total bilirubin level increased) would be higher for the combination than either product alone. For this use case example, data were incomplete for two PTs (alkaline phosphatase increased and weight decreased), thereby not allowing validation of the prediction. Of the remaining six PTs, the combination data showed that the frequency of all-grade events was higher for only four of the six PTs (aspartate aminotransferase increased, alanine aminotransferase increased, blood creatinine increased, lipase increased) while the frequency was not higher for the remaining two PTs (amylase increased [product A: 25%, product B: 8%, combination: 21% all grades] and total bilirubin level increased [A: 21%, B: 6%, combination: 21% all grades]). In both of these instances, the frequency of all-grade events was higher for the combination compared with product B alone, but was scored as inaccurate because it was not higher than that for product A alone.

Furthermore, we predicted that the incidence of all-grade electrolyte disturbance (i.e., select PTs in the Metabolism and Nutrient Disorders SOC “metabol”) may be increased for the combination based on a predicted increase in GI toxicity. Although data were limited for the combination, we were able to validate the accuracy of this prediction for the PTs of hyponatremia (product A: 13%, product B: frequency of Common [14] equivalent to 1–10%, combination: 38% all grades) and hypokalemia (A: 15%, B: not reported as an ADR, combination: 35% all grades).