Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models
DDiscovering Synergistic Drug Combinations forCOVID with Biological Bottleneck Models
Wengong Jin Regina Barzilay Tommi Jaakkola
CSAIL, Masssachusetts Institute of Technology {wengong,regina,tommi}@csail.mit.edu
Abstract
Drug combinations play an important role in therapeutics due to its better efficacyand reduced toxicity. Recent approaches have applied machine learning to identifysynergistic combinations for cancer, but they are not applicable to new diseaseswith limited combination data. Given that drug synergy is closely tied to biologicaltargets, we propose a biological bottleneck model that jointly learns drug-targetinteraction and synergy. The model consists of two parts: a drug-target interactionand target-disease association module. This design enables the model to explain how a biological target affects drug synergy. By utilizing additional biologicalinformation, our model achieves 0.78 test AUC in drug synergy prediction usingonly 90 COVID drug combinations for training. We experimentally tested themodel predictions in the U.S. National Center for Advancing Translational Sciences(NCATS) facilities and discovered two novel drug combinations (Remdesivir +Reserpine and Remdesivir + IQ-1S) with strong synergy in vitro.
Combination therapies have shown to be more effective than single drugs in multiple diseases suchas HIV and tuberculosis [25, 28]. Synergistic combinations can improve both potency and efficacy,either achieving stronger therapeutic effects and/or decreasing dosage thereby reducing side-effects.In the times of current pandemic, finding a successful combination of approved molecules have anadditional benefit over designing a de-novo molecule: time to clinical adoption. Approved drugs aretypically commercially available and have well studied safety profiles. Taken in aggregate, theseconsiderations motivate us to explore combination therapies for COVID antivirals.Since exploring the space of combinations via high-throughput screening is prohibitively expensiveas it involves combinatorial search, in-silico screening based on machine learning is an appealingalternative. In fact, a number of such methods have been reported in the literature [22, 26]. Thesetechniques have been shown effective when the model was provided with large amounts of trainingdata capturing synergy of various combinations. Unfortunately, this requirement prevents us fromutilizing these techniques for many diseases where such data is not available. Therefore, it is crucialto reduce data dependence to make combination algorithm applicable in multiple therapeutic contexts.In this paper, we present a novel algorithm for finding combinations that achieves this goal. Our mainhypothesis is that by explicitly modeling interaction between compounds and the biological targets,we can significantly decrease dependence on combination training data. The proposed biologicalbottleneck model has two components. The first component models drug-target interactions (DTI)predicting which targets are inhibited by a compound. It is trained on individual compounds since DTIinformation is readily available for multiple targets across multiple diseases. Our second componentfocuses on modeling target-disease association. It is a simple linear function which enables the modelto explain how much a biological targets affects synergistic activity.
Machine Learning for Molecules Workshop at NeurIPS 2020. https://ml4molecules.github.io a r X i v : . [ q - b i o . B M ] N ov ynergistic drug combinations Remdesivir + Reserpine + Remdesivir + IQ-1S
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Figure 1: Our model discovered two novel drug combinations (Remdesivir + Reserpine and Remde-sivir + IQ-1S) that show strong synergy in Vero E6 cells.We develop our model using single agent and drug combination data from various sources. It incorpo-rates known COVID biological targets [9] and their corresponding drug-target activity collected fromChEMBL [7]. With only 90 COVID drug combinations for training, our model achieves 0.78 testAUC on the SARS-CoV-2 combination screen from Bobrowski et al. [2]. Moreover, incorporatingknown COVID targets yields 10% relative increase in test accuracy. Lastly, we experimentallytested our model predictions in the NCATS facilities and discovered two novel drug combinations(Remdesivir + Reserpine and Remdesivir + IQ-1S) with strong synergy in Vero E6 cells (see Figure 1).
Existing approach on drug synergy prediction can be roughly divided into two categories:•
Supervised learning : In this approach, a model is trained on combination data generated from high-throughput screens. For example, Preuer et al. [22] trained a deep neural network on a large-scaleoncology screen [21] (23K training examples) to predict anti-cancer drug synergy. Xia et al. [26]and Sidorov et al. [24] trained deep neural networks to predict anti-cancer drug synergy on a largerdataset compiled by NCI-ALMANAC [12], which contains around 300K training examples across40 different cell lines.•
Biological networks : Another category of drug synergy models are based on biological networks.Their assumption is that drugs with complementary mechanism of actions are more likely to besynergistic. For instance, Cheng et al. [3] and Zhou et al. [29] proposed to model synergy usingdistance metrics over drug-target interaction and protein-protein interaction networks.The major challenge of supervised approaches is the lack of combination data. For many diseasessuch as COVID and tuberculosis, the amount of drug combination data is very limited (less than200) [2, 28]. Deep models are prone to over-fitting in this low-resource scenario. Moreover, as thenumber of pair-wise combinations grows quadratically with the number of drugs, the largest existingcombination screen for cancer [12] only covers around 100 different drugs. This significantly limitsthe ability of trained models to generalize to new drugs outside of the training set. On the other hand,while network-driven methods have a wider coverage over the chemical space, they cannot makepredictions on new compounds outside of the network (i.e., drugs without target interaction data).We propose a new method that combines the merit of both approaches while addressing theirlimitations. As drug interaction is often characterized by biological targets, our model is trained topredict both drug-target interaction and drug synergy. This enables us to make predictions on newcompounds even if their drug-target interaction is unknown. This also addresses the data scarcitychallenge since there are abundant drug-target interaction data available.
In this section, we describe our model architecture for drug combinations. A drug combination iscalled synergistic if its antiviral effect is greater than the sum of the individual effects. Drug synergyarises from various types of drug interaction. For example, two drugs can be synergistic when theyinteract with different sets of biological targets or pathways. Indeed, most of the anti-HIV drugcombinations, such as Dolutegravir and Lamivudine, are drugs with different mechanisms of actions(i.e., interacting with different biological targets). To account for this inductive bias, it is crucial tomodel the interaction between drugs and biological targets in our model architecture.2
TI vector z AB Antiviral activity p AB bliss z A z B Drug ADrug B Drug-target interaction Target-disease association
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Biological Bottleneck
Figure 2: Our biological bottleneck model is composed of two modules: a drug-target interaction(DTI) and target-disease association module. The antiviral effect of a combination is predicted ontheir DTI vector z AB , which is computed from the DTI vectors z A , z B of each individual drug. TheDTI vector characterizes the drug-target interaction profile of a given drug.Motivated by these observations, we propose to decompose our model into two parts: a drug-targetinteraction (DTI) module φ and a target-disease association module f . The DTI module predicts thebiological targets activated by a given compound. The target-disease association module learns howa biological target is related to the disease. The vocabulary of biological targets are chosen by expertsin advance. To introduce our method, we first describe how these two modules are used to predictantiviral activity of single compounds and then extend it to drug combinations. We represent a drug as a graph G , whose nodes and edges represent atoms and bonds. To predict theantiviral effect of a single drug A , our model needs to accomplish two tasks: 1) predict its interactionwith biological targets V = { t , · · · , t m } ; 2) learn the relevance of each target t i to the disease. Drug-target interaction
We parametrize the DTI module φ as a graph convolutional network(GCN) [5, 8]. The GCN translates a molecular graph G A into a continuous vector through directedmessage passing operations [27], which associate hidden vectors h v with each node v and updatesthese vectors by passing messages h uv over edges ( u, v ) . The output of φ is a vector z A representingthe biological targets activated by drug A : z A = σ (cid:0) MLP (cid:0) (cid:88) v ∈G A h v (cid:1)(cid:1) { h v } = GCN( G A ) (1)where σ ( · ) is a sigmoid function and MLP is a two-layer feed-forward network. Each element z A,k represents the probability of drug A inhibits target t k . Each target t k is associated with a drug-targetinteraction dataset D k = { ( X i , y i ) } , where y i = 1 if a drug X i is interacts with target t k . We willtrain this module on the DTI dataset of all biological targets in the vocabulary. Target-disease association
We parametrize the target-disease association module f as a simplelinear layer ( w , b ) due to its interpretability. As shown in Figure 2, our model predicts the antiviralactivity of a drug A as: p A = f ( z A ) = σ ( w (cid:62) z A + b ) (2) Synergy are often quantified under Bliss synergy score [1]. Suppose the individual antiviral effectof drugs A and B are p A , p B . The expected effect of combination ( A, B ) is given as e AB = p A + p B − p A p B . A drug combination ( A, B ) is synergistic if its observed effect p AB > e AB .Following this definition, we introduce a new Bliss layer to predict the synergistic effect of a drugcombination ( A, B ) . Given two drugs and their predicted DTI vectors z A , z B , the Bliss layercomputes the DTI vector z AB as z AB = z A + z B − z A (cid:12) z B (3)where (cid:12) stands for element-wise multiplication. With this aggregation function, a drug combinationwill benefit most from complementary targets. If only one drug is active to target t i (e.g., z A,i =1 , z B,i = 0 ), the combination ( A, B ) is still active to t i ( z AB,i = 1 ). In other words, the set of activetargets for ( A, B ) is the union of active targets of the two drugs.3iven a drug combination ( A, B ) , our model predicts its antiviral activity as: p AB = f ( z AB ) = σ ( w (cid:62) z AB + b ) (4)Following the Bliss independence model, we predict the synergy score of a combination as p AB − e AB ,where e AB = p A + p B − p A p B . Intuitively, a combination is more likely to be synergistic if theyhave complementary targets with high target-disease association score. In order to predict synergy, it is important to incorporate all the relevant biological targets into ourmodel. However, this is challenging for two reasons: First, most biological targets do not havedrug-target interaction data and thus cannot be incorporated in our model. Second, current biologicalunderstanding of a disease may be incomplete. For instance, Riva et al. [23] reported around 50 newbiological targets related to COVID antiviral activity, but they are not reported in the previous workby Gordon et al. [9].To this end, we propose to include additional latent targets in the bottleneck layer that are learnedindirectly from single-agent and combination data. Specifically, we expand the dimension of z A tobe greater than the total number of considered targets in V . The first m entries in z A corresponds tothe real biological targets and the other entries are latent targets. As we will show in the experiments,it is possible for us to interpret new biological targets related to given diseases. Our training loss L = λ DTI (cid:96)
DTI + λ S (cid:96) S + (cid:96) C consists of three components. First, the drug-targetinteraction loss (cid:96) DTI enforces the DTI vector z A to be biologically meaningful. This is calculated foreach target based on its DTI dataset D k = { ( X i , y ti ) } . The DTI module φ is trained to minimize: (cid:96) DTI = (cid:88) k (cid:88) ( X i ,y ti ) ∈D k (cid:96) ( z X i ,k , y ti ) (5)Second, our model is trained on single-agent data D S = { ( X , y s ) , · · · , ( X n , y sn ) } . Each molecule x i is labeled with its antiviral activity (active/inactive). Both modules φ, f are trained to minimize (cid:96) S = (cid:88) ( X i ,y si ) ∈D S (cid:96) ( f ( φ ( X i )) , y si ) (6)Lastly, the model is trained on drug combination data D C = { ( A i , B i , y i ) } . Each drug combination ( A i , B i ) has a synergy label y ci , where y ci = 1 means it is synergistic and y ci = 0 additive orantagonistic. We train both modules φ, f to minimize (cid:96) C = (cid:88) ( A i ,B i ,y ci ) ∈D C (cid:96) ( p A i B i − e A i B i , y ci ) (7) Multi-disease training
Since COVID is a new disease, its drug combination data is very limited.To address the low-resource challenge, we utilize additional drug synergy data from other viraldiseases such as HIV. Specifically, we augment the model with HIV biological targets as well as HIVsingle-agent and drug combination data. The DTI module φ now outputs a DTI vector z dA for eachdisease d . φ is shared across two diseases and trained to learn drug-target interaction for all diseases.Since each disease operates on different targets, we create a target-disease association module f D foreach disease. Let (cid:96) d DTI , (cid:96) dS , (cid:96) dC be the losses for each disease d ∈ { COVID, HIV } . Our final trainingloss becomes L multi = (cid:88) d λ (cid:96) d DTI + λ (cid:96) dS + (cid:96) dC (8) SARS-CoV-2 Data
For SARS-CoV-2 infection, we consider three types of biological targets in ourtarget vocabulary V = { t , · · · , t m } : 4 TI vector z A Antiviral activity p A Drug-target interaction Target-disease association } Drug A A U R O C T a r g e t - C O V I D A ss o c i a t i o n -0.9-0.6-0.300.30.60.9 Positive targets 1. MARK2 6. PSMD8 2. GLA 7. CSNK2B 3. IDE 4. MARK3 5. HDAC2
Figure 3:
Left : Results on SARS-CoV-2 combination test set. Our model (+all) outperforms all otherbaselines.
Right : Seven targets that positively contributes COVID drug synergy.•
Viral proteases : Replication of SARS-CoV-2 virus requires the processing of two polyproteins bytwo virally encoded proteases: chymotrypsin-like protease (3CLpro) and papain-like protease (PL-pro). Inhibitors that block either protease could inhibit viral replication. We have compiled 3CLproenzymatic activity [15] and PLpro inhibition [4] data made public by NCATS and ReframeDB.•
Viral entry proteins : SARS-CoV-2 cell entry depends on angiotensin converting enzyme 2(ACE2) [11]. Inhibiting ACE2 enzyme or the interaction between SARS-CoV-2 and ACE2could block viral entry. To this end, we utilize ACE2 enzymatic activity [16] and Spike-ACE2protein-protein interaction [19] from NCATS.•
Host proteins : Gordon et al. [9] identified 335 human proteins physically associated with SARS-CoV-2 viral proteins. Inhibitors for these proteins may also hinder viral replication. Among theseproteins, we selected 31 proteins that have sufficient amount of drug-target interaction data in theChEMBL database (i.e., both positive and negative interactions).The above drug-target interaction data contains around 20K compounds in total. Our training data forSARS-CoV-2 utilizes another two assays:•
Single-agent Activity : We use the NCATS CPE assay in VeroE6 cells [17], which contains around10K compounds and 320 hits with EC50 ≤ µ M.•
Drug Combination : NCATS performed two combination assays in VeroE6 cells, which contain160 two-drug combinations [2, 18]. Riva et al. [23] also analyzed synergy between Remdesivir and20 active compounds identified from their high-throughput screen.
HIV Data
The training data for HIV comes from the following assays:•
Drug-target Interaction : Existing anti-HIV drugs mainly target viral proteins (HIV-1 protease,integrase and reverse transcriptase) or host proteins involved in viral entry (CCR5, CXCR4 andCD4). We compiled DTI data for these six targets from ChEMBL.•
Single-agent Activity : NCI conducted an anti-HIV assay [20] with 35K compounds, among which309 compounds are active (EC50 ≤ µ M).•
Drug Combination : Tan et al. [25] conducted high-throughput screen for HIV drug combinations.The dataset contains 114 two-drug combinations.
Evaluation Protocol
Since our goal is to predict synergy against SARS-CoV-2, our validationand test set only consist of SARS-CoV-2 combinations. All the drug-target interaction, single-drugactivity and HIV data are used for training only. Our validation set contains 20 combinations fromRiva et al. [23] and test set contains 72 combinations from Bobrowski et al. [2]. The training setcontains 90 SARS-CoV-2 combinations from [18], where we remove combinations that appear inboth the training and test set.
Hyperparameters
For DTI module φ , we adopt default hyperparameters from Yang et al. [27], withhidden dimension 300 and three message passing iterations. We set the dimension of DTI vector | z | = 100 , with 42 real biological targets (SARS-CoV-2 and HIV) and 58 latent targets, so that thenumber of real and latent targets are roughly equal. We set λ = 10 , λ = 0 . for our final model.5igure 4: Two new drug combinations are discovered through our model: Remdesivir + Reserpineand Remdesivir + IQ-1S. Figures on the left show the dose response matrix and bliss synergy matrixfor Remdesivir + Reserpine. Figures on the right show the same information for Remdesivir + IQ-1S. To show the effectiveness of different components, we compare with the following baselines:• A GCN trained only on SARS-CoV-2 single-agent and combination data ( λ DTI = 0 , | z | = 100 ).• +DTI: A GCN trained only on SARS-CoV-2 single-agent, combination as well as drug-targetinteraction data ( λ DTI = 10 , | z | = 100 ).• +MultiD: A GCN trained on both SARS-CoV-2 and HIV data (single-agent + combination), butwithout drug-target interaction data ( λ DTI = 0 , | z | = 100 ).• +All,-latent: A GCN trained on both SARS-CoV-2 and HIV data (single-agent + combination +drug-target interaction), but the latent targets are removed ( λ DTI = 10 , | z | = 42 ).• +All: A GCN trained on both SARS-CoV-2 and HIV data (single-agent + combination + drug-targetinteraction) ( λ DTI = 10 , | z | = 100 ).Our results are shown in Figure 3. As expected, the GCN baseline performs poorly, with . ± . AUC. Adding drug-target interaction data (+DTI) improves the AUC to . ± . . Adding HIVdata (+MultiD) improves the AUC to . ± . . Our final model, trained with both HIV anddrug-target interaction (+All), achieves the best AUC of . ± . . This validates the advantageof adding drug-target interaction data and multi-disease training. Note that if we remove the latenttargets (+All,-latent), the performance decreases to . ± . . This also shows the importanceof using latent targets to complement missing biological information.In Figure 3, we report the learned target-disease association score for all COVID targets. There areseven targets positively correlated with COVID antiviral activity. According to Gordon et al. [9],these targets interact with SARS-CoV-2 Orf9b, Nsp14, Nsp5 and N viral proteins. The wide range ofhost-viral protein interaction indicates that drug synergy arises from different modes of action. We applied our best model to predict synergy of drug combinations in the NCATS compound library.Given limited experimental resources, we only evaluated pairwise combinations between potentdrugs with single-agent IC50 less than 30uM. This gave us around 11600 drug combinations rankedaccording to predicted synergy score with the highest scores. We selected the top 30 candidatesand experimentally tested them in NCATS facilities. We successfully discovered two new drugcombinations (Remdesivir + Reserpine and Remdesivir + IQ-1S) with strong synergy in Vero E6cells. The dose response and bliss synergy matrix are shown in Figure 4.
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