Teva Sildenafil: Generic Viagra, Pros and Cons, and Side Effects

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A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan

Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with ‘look-alike and sound-alike’ (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them.

Methods

We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs.

Results

Our results showed that the total training time for the front-side model and back-side model was 5 h 34 min and 7 h 42 min, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%).

Conclusions

In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.

Background

Issuing of correct prescriptions is the mainstay of patient safety. Medication errors are the most important problem that influences safety in health care [1]. The most common medication errors are caused by human factors, such as fatigue and inadequate knowledge [2]. In particular, look-alike and sound-alike (LASA) is the lead error at the level of pharmacists or physicians. A good policy to prevent LASA is to change drug names and their packaging [3]. Researchers used chart reviews and mathematical methods to identify problematic pairs of drug names, and constructed an automated detection system to detect and prevent LASA errors [4]. Unfortunately, major problems remain in drug identification: many drugs look alike; drugs are relatively small in size; and a large number of drugs need to be identified. Existing identification solutions still have their limitations [5,6,7].

However, some assistive tools do exist. Automated dispensing cabinets (ADCs) represent a solution that dispenses drugs automatically [8, 9], and there are many ADC technologies in existence. Some studies have used barcoding for drug identification and prevention of medication errors [9]. Devices that employ radio-frequency identification (RFID) and Bluetooth to identify the positions of drugs have been designed [8]. Most large hospitals use robots; however, there are fewer robots than needed in hospitals with fewer than 100 beds [10]. Other major problems with the use of ADCs are the development of suitable software that can identify drugs accurately without the need for pre-processing of drugs or a large space in the pharmaceutical department before applying the systems. In addition, it needs to be ensured that these systems will not increase the burden on pharmacists during the prescription process [11, 12].

Alternatively, image-based solutions have been developed. Traditional image recognition finds features through algorithms and then classifies images using certain classifiers [13, 14]. Lee et al. encoded color and shape into a three-dimensional histogram and geometric matrix, and encoded the imprint as a feature vector through a Scale Invariant Feature Transform (SIFT) descriptor and a Multi-scale Local Binary Pattern (MLBP) [15]. Taran et al. [16] proposed the use of a variety of traditional artificial feature integration methods to extract high-dimensional drug features from images to achieve identification of blister packages. Saitoh [17] used the local feature and nearest-neighbor search method to sort images of blister packages in a database according to input test images, and sorted blister packages with the most similar shapes and colors through voting scores.

Most significantly, thanks to the vigorous development of Graphics Processing Units (GPUs) for parallel computing, a current mainstream process is to adopt deep learning methods to replace traditional classifiers. Examples include biomedical imaging and wave recognition [18, 19]; speech recognition [20, 21]; biomedical signal detection [18, 19, 22]; cancer identification [19, 22, 23]; potential drug discovery [24, 25]; and adverse drug effects [26]. Images of the drug are pre-processed to obtain the correct viewing angle and drug separation, and the characteristics of the pills are established manually [27]. Drug identification is implemented in a framework based on a Deep Convolutional Network (DCN), and achieved good recognition. In addition, another method of pill identification first finds the location and area of ​​the drug by detecting the edge contour of the pill [27]; then, through a variety of data augmentation methods such as color shift, size adjustment, Gaussian blur, etc., more training samples are generated to solve the problem of sparse training samples. Three GoogLeNet deep learning networks are used as the main classifiers to train the color, shape and characteristics of the pills, and the recognition results of the three models are then combined to obtain the final recognition results [28].

This study focused on the problem of drug identification using visual images of blister packages. We constructed a Deep Learning Drug Identification (DLDI) model that identifies drugs automatically and can assist pharmacists in dispensing prescriptions correctly. Our goal was to illustrate how ‘look-alike’ error can be captured and explained by a convolution-based deep learning network whose working mechanism is in much similarity to the human visionary recognition capability. Subsequently, appropriate solution to extract more detailed nuance differences can be utilized in distinguishing look-alike objects.

Methods

To investigate how a deep learning network identifies object types, a dataset containing images both sides of 250 types of blister packages were collected for training and testing data of a deep learning network. Identification results in terms of precision, recall, and the combined F1-score were computed, where an identification error can be regarded as an error due to look-alike cases.

Data resources

This study collected drugs from the Out-Patient Department (OPD) of a medical center. Of the 272 kinds of drug, this study focused only on recognition of pharmaceutical blister packages. As such, 6 classes of drug packaging (Fig. 1), totaling 32 kinds of drug, were excluded, as follows: clip chain bags, powder bags, foil packaging bags, transparent bags, paper packages, and bottle packaging. The remaining 250 drugs with blister packaging were considered.

We aimed to identify blister packages by their images, photographed using a camera from different angles. In collecting the training set, 72 images were taken for each side of each type of drug: the camera focused from 9 different angles, with 8 different rotation directions of the drug shown in the images (Fig. 2). Both front-side and back-side images were taken for each drug, resulting in a total of 36,000 images as the training data for deep learning. Images of the front sides of packages contained the shapes and colors of the pills or tablets, whereas images of the back sides contained mostly texture patterns of the drugs or logos of pharmaceutical companies. These images were used to train CNN networks, the deep learning networks, for object identification.

Deep learning architecture

The concept of the Convolution Neural Network (CNN) was proposed by LeCun and others in 1989. These deep learning networks usually consist of convolutional layers, pooling layers and fully-connected layers [29]. As the convolutional layers and the pooling layers in the network architecture enhance the relationship between pattern recognition and adjacent data, a CNN can be applied to signal types such as images and sounds. Through multi-layer convolution and pooling, the extracted features are treated as inputs, and then forwarded to one or more fully-connected layers for classification. Unfortunately, the simple CNN is not effective for more complex images. Krizhevsky et al. [30] reconstructed a CNN in 2012, and in CNN-based networks, the deep learning framework of “object detection” has also been continuously improved. R-CNN was the first successful CNN-based object detection method, but the speed of detection was very slow [31]. Later, the Fast and Faster R-CNN were constructed [32], optimized on the basis of R-CNN, and the speed and accuracy were improved significantly.

Software and hardware devices

This study used You Only Look Once (the abbreviation ‘YOLO’ having been proposed by Redmon et al. in 2015) as the solution framework for deep learning [33]. An end-to-end structure was adopted, and compared with the general deep learning method, YOLO focuses on both the area prediction part of detection and the category prediction part for classification. YOLO integrates detection and classification into the same neural network model, with fast and accurate target detection and recognition. These deep learning techniques employ the following features: batch normalization for faster convergence; passthrough for the features identification increasing; hi-res classifier to increase the resolution of the images; direct location prediction to strengthen the stabilization of position prediction; and multi-scale training to improve both speed and accuracy. The SENet and ResNet experiments in this study used the Kubuntu 14.04 system and the Darknet framework in the Caffe structure of Windows 7, which is a special hardware device host for deep learning. This study also employed an Intel® I7–6770 Eight-Core Processor (CPU), 16 GB RAM, and a NVIDIA GTX 1080 Graphic Processing Unit (GPU).

Experimental design

For model evaluation, this study partitioned the collected data into separate training and testing sets. The training set trained the deep network to generate models, while the testing set evaluated the performance of the constructed models. We randomly choose three-quarters of the 72 pictures of each type of drug for inclusion in the training set, and the remaining quarter were included in the testing set, with 13,500 images in total in the training set and 4500 images in the testing set. This study trained 100 models for each of the front-side and back-side images using the training set. The best model was chosen, which was defined as the model with the greatest accuracy (highest F1 measure) and the fastest speed (fewest Epochs). This study also standardized the YOLO v2 protocol for both the training and testing datasets in each model. All images were converted into 224 × 224 pixels. Neither data augmentation nor pre-training of the model were performed during training. The batch size was 8, meaning that parameters were re-adjusted every 8 images. The highest training frequency was 100 Epochs, one Epoch meaning that the deep network ran all the pictures during training. The parameters were saved after every Epoch was completed (Table 1).

Deep learning models share cognitive capabilities similar to those of the human eye, and what confuses a deep learning network can also confuse the human eye. As such, in order to identify look-alike blister packages, we created confusion matrixes, which recorded the actual blister packages that were identified, correctly or not. Correct matches were listed on the diagonal of the matrix, whereas cases of missed identification were marked by non-zero values off the diagonal. The higher the number, the greater the chance of misidentification of blister packages of drugs.

For example, assume that there is a system for classifying three different drugs (Table 3). Suppose that there are 28 drugs in total: 9 drug A, 6 drug B, and 13 drug C. In this confusion matrix, there are actually nine drug A, but three of them are misidentified as drug B. For drug B, one of the drugs is misidentified as drug C, and two are misidentified as drug A. The confusion matrix shows that it is more difficult to distinguish between drug A and drug B, but easier to distinguish drug C from the other drugs. In the confusion matrix, correct identifications are on the diagonal; in contrast, misidentified ones are the non-zero terms off the diagonal.

According to the identification results recorded in the confusion matrix for the back-side model, the blister package of Ciprofloxacin (Fig.4e) was misidentified as URSOdeoxycholic acid (Fig. 4f), and the blister package of Alprazolam (Fig. 4g) was misidentified as Rivotril (CLONAZEPAM) (Fig. 4h). These two misidentifications were due to the fact that the backs of the blister packages were wrapped in aluminum foil, and the textual patterns on the back-side were of the same color, without significant difference.

Discussion

The study provides a qualitative examination regarding how look-alike blister packages are recognized or constrained by deep learning networks that are reminiscent of human visionary cognition capability. With racing speed of progress in deep learning techniques, it is expected that more accurate deep learning solutions will emerge to distinguish nuance image features among different object types, thus mitigating if not solving the dispensing error caused by look-alike blister packages.

Image based techniques, being non-intrusive and without resort to additional devices like RFID tag or bar code, have been a preferred solution to object identification problems. Traditional image-based solutions by computer vision rely on well-defined hierarchical features for effective comparison [34, 35]. Some of the research work from literature reported performance of less than 80% of accuracy with limited number of types of less than 50 [15, 36]. In contrast, the distinguishing features reported in this study are learned by adjusting network parameters through fitting training data, the process being much similar to human visionary recognition process, thus achieving accuracy better than 90% among 250 types. With the advent of deep learning technique, identification witnesses a revolutionary shift which can benefit blister package identification critical to dispensing safety.

This study proposed a novel deep learning drug identification (DLDI) model that delivered satisfactory results for drug identification based on images of blister packages. The results of this study showed that identification by “deep learning” is no less accurate than identification by the human eye. The CNN simulates the response of neurons in the human brain to signals by performing various mathematical operations on features to complete the classification. Repetition of these processes achieves the purpose of recognition. In earlier studies, features were defined subjectively to identify blister packages of drugs [17]. Deep learning allows learning of features automatically, without the need to define features of drugs before machine learning. This advantage eliminates human error and assists pharmacists to identify drugs correctly. Deep learning enables identification of the characteristics of individual drugs clearly and recognizes the drugs that pharmacists/humans consider look alike. Just one or two cameras in dispensing cabinets are required, and medication errors will be prevented.

Referring to Table 2, this study found that back-side images of blister packages of drugs were better than front-side images for identification purposes. While back-side took more training time to better distinguish textual features, based on 4500 test images evenly distributed over 250 types, the associated performance criteria of: precision, recall, and F1 score are all better than that by the front-side images. This is because the information on the back of the packages includes the pharmaceutical company, drug name, dose, and logo in larger text than on the front of the package, which only presents information regarding the color and shape of the pills. The front of the drug packaging contains some three-dimensional information with regards to drug shape. However, some blister packages were not easily recognized by the deep learning network, and were more likely to be confused according to the confusion matrix. These unrecognizable blister packages correspond to look-alike blister packages recognized by the human eye. In the future, we will employ a convolution kernel to identify data features to generate signals and perform a comparison with the human eye.

There are many kinds of drug packages that need to be identified: pills; blister packaging; clip chain bags; powder bags; foil packaging bags; transparent bags; paper packages; bottle packaging, etc. For medication adherence and drug preservation, most drugs are packaged in blisters [37]. Moreover, for some drugs, infrared spectrum analysis of tablets in intact blisters is performed to distinguish between genuine and counterfeit samples [38]. DLDI models may also be applied to automated dispensing cabinets (ADCs), and can be employed in cooperation with both pharmacists and robots. Some robots have cameras, which would be useful for application of our model for drug identification. In the future, we will construct a blister-package identification model that takes account of both sides of the packaging, which will contain more information than just a single side for identification. The identification accuracy may also be increased by use of three-dimensional images of drugs or images with different spectrums for deep learning.

There are some considerations for future studies. First, this study only examined blister-packaged drugs, and used the whole of the blister packages for identification. This model cannot be used to identify blister packages when held in the hand, or trimmed blister packages. Moreover, other types of drug packaging need to be studied. In some cases, the pill size and shape were too familiar to identify. One of the aims of future study is to address these issues. Second, the training time was too long, with more than 5 h required for training the models in this study. More time is required if more than one kind of spectrum is used, and a more effective program is needed to train the models. Third, re-training would be needed if one or more new drugs are added in this model. In the future, we hope to develop a system in which only “PARTIAL” training is required when drugs are changed or added.

Conclusion

Our goal was to illustrate how ‘look-alike’ error can be captured and explained by a convolution-based deep learning network whose working mechanism is in much similarity to the human visionary recognition capability. Subsequently, appropriate solution to extract more detailed nuance differences can be utilized in distinguishing look-alike objects. With an accuracy greater than 90%, the results of this study may be applied to the real environment, and may assist pharmacists to identify drugs and prevent medication errors caused by look-alike blister packages. The results of this study can also form the core software for robots, allowing filling of prescriptions automatically and preventing medication errors.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to that was belonged to the MacKay Memorial Hospital but are available from the corresponding author on reasonable request.

Teva Sildenafil: Generic Viagra, Pros and Cons, and Side Effects

Viagra is so popular that its name has become synonymous with ED treatment — just like Ziploc has with plastic bags. But the active ingredient in Viagra is sildenafil.

Pfizer Pharmaceuticals patented Viagra in 1996. It’s been a wild success since it entered the market in 1998.

But in 2013, Teva Pharmaceuticals made a generic form of sildenafil to compete with Viagra.

After a long court battle and approval by the Food and Drug Administration (FDA), Pfizer paid Teva to not release their generic form until 2017.

So, here’s what you should know about it — how it works, how it compares to Viagra, and what precautions you should take.

Sildenafil is a phosphodiesterase type 5 (PDE5) inhibitor. This means that it helps block the PDE5 enzyme that affects certain muscles in your penis and heart.

How it works for ED

PDE5 can restrict blood flow to blood vessels in the spongy penis tissue called corpus cavernosa. This tissue helps your penis get erect when you’re aroused. During arousal, it becomes filled with blood.

Teva sildenafil and other similar medications help stop PDE5 from restricting blood flow into these blood vessels. By doing this, it helps your penis get enough blood to maintain an erection.

How it works for PAH

PAH can cause smooth muscle in your lungs to become inflamed and restrict blood flow in certain lung arteries.

PDE5 inhibitors like Teva sildenafil help dilate these arteries and reduce your blood pressure.

Dosage forms

You can get Teva sildenafil with a prescription from your doctor. It comes in the following tablet sizes:

It also comes as an oral suspension (liquid form) or an injection given by your doctor.

Teva sildenafil is prescribed to people who have difficulties having an erection or staying erect throughout a sexual encounter. This medication is very similar to Viagra and works to help with erectile dysfunction by inhibiting PDE5, an enzyme that can cause the penis to not become erect or lose an erection prematurely.

Since Teva sildenafil is available as a generic drug, one of the major benefits over Viagra is its lower cost. This means that a person experiencing ED can receive many of the benefits of taking Viagra at a reduced cost. This is especially beneficial if prescription Viagra is outside of a person’s budget.

While a majority of Teva sildenafil users take the medication to treat ED, it is also approved to treat pulmonary arterial hypertension (PAH) as well.

Your doctor may choose to prescribe it for treating PAH, even when symptoms of ED are not present. Additionally, research shows Teva sildenafil may be effective in treating both sexes for PAH .

Teva sildenafil only works properly if you take it about an hour before you have sex.

You need to be sexually aroused to get erect and for the medication to sustain the erection. Teva sildenafil takes effect about 30 to 60 minutes after you’ve taken it.

Like other medications, the effects of Teva sildenafil might be delayed if you eat right before you take it. This is because food competes for absorption from the stomach into the bloodstream.

Spoiler alert: There’s no real difference between Viagra and Teva sildenafil.

Remember that the name of the drug sold as Viagra is sildenafil citrate. The difference in names is really just for marketing reasons. The main functional ingredient is the same.

Teva sildenafil is a generic version of Viagra that’s sold and branded by a different company. This means that it’s typically sold for much lower prices since no marketing dollars are needed to sell it.

There are also some medical reasons for the distinction between Teva sildenafil and Viagra.

Sildenafil and Viagra are both used to treat erectile dysfunction, but sildenafil can also be prescribed to treat PAH and reduce blood pressure in the lungs. Viagra is currently only legally approved for use to treat ED.

Teva sildenafil pills are white, not blue

There’s also a visual difference in the two. Viagra is infamous for being the “little blue pill.” But Teva sildenafil tablets are simply off-white or white in color.

Before you decide to use Teva sildenafil, talk to a doctor. They can determine if you’re a good candidate, what dose is right for you, and how to introduce it into your routine.

You should also consider the pros and cons of this medication, as summarized below.

Pros

Cons

  • needs to be taken at least 1 hour before sex, so timing is critical
  • not recommended if you’ve had a stroke or heart attack in the last 6 months
  • not recommended if you take any medications with nitrates, like Isordil or nitroglycerin
  • more side effects and risks if you’re over 65 years old

Teva sildenafil lasts about 2 to 3 hours before your erection begins to subside.

You may be able to get erect from Teva sildenafil for up to 5 hours (or even 18 hours ), depending on how much you take.

Other factors that can affect how long it lasts include:

  • Diet. Many foods and nutrients can affect your blood flow.
  • Lifestyle. Your level of activity and exercise can influence your blood flow.
  • Age. How old you are contributes to your overall health and how efficiently your blood flows.
  • Medications. Many drugs can affect your blood pressure.
  • Overall health. Many underlying health issues can affect your blood flow and other mechanisms that affect your ability to get an erection, such as nerve sensitivity.

You should not take Teva sildenafil if you:

  • are over 65
  • take nitrates
  • have an existing heart condition
  • take any other medications that might interact with Teva sildenafil
  • take alpha-blockers for high blood pressure
  • have been diagnosed with Peyronie’s disease
  • have tinnitus (ringing in the ears)
  • have liver or kidney disease
  • have sickle cell anemia

While severe side effects are generally uncommon when taking Teva sildenafil, it can interact negatively when combined with other drugs and substances. Some of the most common conflicting substances to be aware of are:

  • alpha-blockers, like prazosin (Minipress), terazosin (Hytrin), and doxazosin (Cardura)
  • beta-blockers, like atenolol (Tenormin), propranolol (Inderal LA), and nadolol (Corgard)
  • other ED treatment medications
  • some high blood pressure medications
  • blood thinners, like warfarin (Coumadin and Jantoven)
  • nitrates, like nitroglycerin, isosorbide mononitrate, and isosorbide dinitrate
  • some seizure medications like carbamazepine (Carbatrol and Tegretol) and phenobarbital

Whenever you start a new medication, it’s important to tell your doctor about any other medications you’re taking so if needed, they can adjust your other medications or dosage, or monitor how your system reacts to the new medication.

Teva sildenafil and other similar medications have the following possible side effects and risks:

  • nausea
  • dizziness
  • sudden rash
  • dangerously low blood pressure
  • sinus congestion
  • difficulty digesting or gas
  • head pain
  • redness or flushing in your face
  • pain in your back
  • sudden loss of hearing or vision
  • priapism (erection that lasts longer than 4 hours and can be painful)

See a doctor if you notice any of the following uncommon or rare side effects when you take Teva sildenafil:

  • sharp or burning chest pain that gets worse over time
  • pain in your bladder
  • stomach pain or burning
  • feelings of tingling, crawling, or numbness
  • blood in your pee
  • pee with an unusual, cloudy consistency
  • peeing a lot more than usual or pain when you pee
  • fatigue
  • swelling in your face, hands, or other extremities

Is Teva sildenafil the same as Viagra?

The short answer is yes. Teva sildenafil and Viagra are pretty much the same and share the same main ingredient — sildenafil citrate. Viagra is a brand-name drug that’s created and manufactured by Pfizer. Teva sildenafil is the generic version of sildenafil citrate that’s manufactured by Teva Pharmaceuticals.

What does Teva sildenafil do?

Teva sildenafil can be used to improve blood flow and inhibit the PDE5 enzyme, which is often responsible for erectile dysfunction. Many people have found that their erectile dysfunction symptoms improve while taking Teva sildenafil. Additionally, Teva sildenafil has been approved to treat pulmonary arterial hypertension.

Is it OK to take 100 mg of Teva sildenafil?

The recommended dosage for Teva sildenafil is 50 mg, but if you are seeing minimal improvements of your symptoms at that level, consider talking with your doctor about increasing the dosage. The dosage can be increased to 100 mg, but it is not recommended to exceed that dosage.

As with any medication, you should not increase the dosage on your own without talking with your doctor first.

How much Teva sildenafil is too much?

The maximum daily dosage is 100 mg, and that amount should not be exceeded because it can lead to severe side effects like:

This is another reason why it is important to discuss other medications that you are taking with your doctor.

For example, you may not know that your other medications contain sildenafil citrate, and when combined with Teva sildenafil, it can push you over the 100 mg threshold within a 24-hour period.

Teva sildenafil is a generic form of the medication that’s also sold as Viagra.

Sildenafil has been shown to be very successful in treating ED and PAH. However, it’s still important to talk with a doctor before you begin taking it.

Sildenafil needs to be taken a certain way to work properly, and it can have a variety of side effects or interactions that are potentially dangerous.

Last medically reviewed on June 15, 2022

How we reviewed this article:

Healthline has strict sourcing guidelines and relies on peer-reviewed studies, academic research institutions, and medical associations. We avoid using tertiary references. You can learn more about how we ensure our content is accurate and current by reading our editorial policy.

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Our experts continually monitor the health and wellness space, and we update our articles when new information becomes available.

Is Sildenafil 20mg an Effective Dose?

Medically reviewed

Table of contents

Sildenafil is a pharmaceutical medication that is FDA-approved to treat erectile dysfunction (ED) in men and pulmonary arterial hypertension (PAH), a form of high blood pressure, in both men and women. It is one of only four phosphodiesterase inhibitors (PDE5 inhibitors) commercially available on the market today. Other PDE5 inhibitors include tadalafil (Cialis), vardenafil (Levitra), and avanafil (Stendra).

PDE5 inhibitors work by blocking the enzymes that inhibit blood flow in parts of your body. When men with erectile dysfunction take sildenafil, it helps the arteries in their penis expand to allow more blood flow and firmer, longer-lasting erections. When people PAH take sildenafil, the medication helps open up any narrowed arteries in their lungs and improve their circulation during periods of exercise.

In this article, I’ll explain what sildenafil is, how it compares to Viagra, and how to know if 20 mg of sildenafil is an effective treatment for you.

What Is Sildenafil?

Sildenafil (also called sildenafil citrate) is a prescription drug used to treat erectile dysfunction in men and pulmonary arterial hypertension (PAH) in men and women. It works by blocking the enzymes that make it more difficult for blood vessels to dilate and circulate freely through your body.

In 1998, Pfizer patented sildenafil under the brand name Viagra and began selling it to consumers worldwide. In 2005, the FDA re-approved sildenafil to treat people with PAH. To make sure that everyone could tell the difference between the two drugs, Pfizer marketed this new use of sildenafil under the name Revatio.

When Pfizer’s patent on sildenafil expired, it allowed cheaper generic forms of the medication to hit the market. These drugs are identical to Viagra and Revatio but are usually sold under the name sildenafil.

tadalafil

What is the most important information I should know about tadalafil?

Taking tadalafil with certain other medicines can cause a sudden and serious decrease in blood pressure.

Do not take tadalafil if you also use riociguat, or a nitrate drug such as nitroglycerin, isosorbide dinitrate, isosorbide mononitrate, or recreational drugs such as “poppers.”

Get medical help at once if you have nausea, chest pain, or dizziness during sex.

What is tadalafil?

The Cialis brand of tadalafil is used in men to treat erectile dysfunction (impotence) and symptoms of benign prostatic hypertrophy (enlarged prostate).

Adcirca and Alyq are used in men and women to treat pulmonary arterial hypertension (PAH) and to improve exercise capacity.

Do not take Cialis for erectile dysfunction if you are taking Adcirca or Alyq for pulmonary arterial hypertension.

Tadalafil may also be used for purposes not listed in this medication guide.

What should I discuss with my healthcare provider before taking tadalafil?

You should not take tadalafil if you are allergic to it, or:

Do not take tadalafil if you are also using a nitrate drug for chest pain or heart problems. This includes nitroglycerin, isosorbide dinitrate, and isosorbide mononitrate. Nitrates are also found in some recreational drugs such as amyl nitrate, butyl nitrate or nitrite (“poppers”). Taking tadalafil with a nitrate medicine can cause a sudden and serious decrease in blood pressure.

Some tadalafil can remain in your bloodstream for 2 or more days after each dose you take (longer if you have liver or kidney disease). Avoid nitrate use during this time.

  • heart problems (chest pain, a heart rhythm disorder, heart failure);
  • a heart attack or stroke;
  • high or low blood pressure;
  • liver disease;
  • kidney disease (or if you are on dialysis);
  • retinitis pigmentosa (an inherited condition of the eye);
  • blindness in one or both eyes;
  • hearing problems;
  • blood circulation problems;
  • a blood cell disorder such as sickle cell anemia, multiple myeloma, or leukemia;
  • pulmonary veno-occlusive disease (PVOD);
  • a physical deformity of the penis (such as Peyronie’s disease), or an erection lasting longer than 4 hours;
  • a stomach ulcer; or
  • health problems that make sexual activity unsafe.

A small number of people taking tadalafil have had sudden vision loss. Most of these people already had eye problems, or had diabetes or other health conditions that can affect blood vessels in the eyes. It is not clear whether tadalafil causes vision loss.

Do not start or stop taking tadalafil during pregnancy without your doctor’s advice. Having pulmonary arterial hypertension (PAH) during pregnancy may cause heart failure, stroke, or other medical problems in both mother and baby. Tell your doctor right away if you become pregnant.

It may not be safe to breastfeed while using this medicine. Ask your doctor about any risk.

Tadalafil is not approved for use by anyone younger than 18 years old.

How should I take tadalafil?

Follow all directions on your prescription label and read all medication guides or instruction sheets. Use the medicine exactly as directed.

When taking Adcirca or Alyq, you may need 2 tablets for a full dose. Take both tablets one after the other. Do not split the dose.

Take Cialis just before sexual activity, but not more than once per day. Cialis can help achieve an erection when sexual stimulation occurs. An erection will not occur just by taking a pill.

Do not break or split a Cialis tablet. Swallow it whole.

Take the medicine at the same time each day, with or without food.

Do not change your tadalafil dose or stop taking this medicine without your doctor’s advice.

Store at room temperature away from moisture and heat.

What happens if I miss a dose?

Since Cialis is used as needed, you are not likely to miss a dose.

If you miss a dose of Adcirca or Alyq, take the medicine as soon as you can, but skip the missed dose if it is almost time for your next dose. Do not take two doses at one time.

What happens if I overdose?

Seek emergency medical attention or call the Poison Help line at 1-800-222-1222.

What should I avoid while taking tadalafil?

Avoid drinking alcohol. It may increase your risk of dizziness or fainting.

Grapefruit may interact with tadalafil and lead to unwanted side effects. Avoid the use of grapefruit products.

What are the possible side effects of tadalafil?

Get emergency medical help if you have signs of an allergic reaction: hives; difficulty breathing; swelling of your face, lips, tongue, or throat.

Stop using tadalafil and call your doctor at once if you have:

  • heart attack symptoms –chest pain or pressure, pain spreading to your jaw or shoulder, nausea, sweating;
  • vision changes or sudden vision loss;
  • ringing in your ears or sudden hearing loss; or
  • an erection is painful or lasts longer than 4 hours (prolonged erection can damage the penis).

Stop and get medical help at once if you have nausea, chest pain, or dizziness during sex. You could be having a life-threatening side effect.

  • headache;
  • flushing (warmth, redness, or tingly feeling);
  • nausea, upset stomach;
  • runny or stuffy nose; or
  • muscle pain, back pain, pain in your arms, legs, or back.

This is not a complete list of side effects and others may occur. Call your doctor for medical advice about side effects. You may report side effects to FDA at 1-800-FDA-1088.

What other drugs will affect tadalafil?

Do not take Cialis with similar medications such as avanafil (Stendra), sildenafil (Viagra) or vardenafil (Levitra). Tell your doctor about all other medications you use for erectile dysfunction.

Tell your doctor about all your other medicines, especially:

  • medicines to treat erectile dysfunction or pulmonary arterial hypertension;
  • drugs to treat high blood pressure or a prostate disorder;
  • an antibiotic such as clarithromycin, erythromycin, rifampin, or telithromycin;
  • antifungal medicine such as ketoconazole or itraconazole;
  • medicine to treat HIV/AIDS, such as ritonavir and others; or
  • seizure medicine such as carbamazepine or phenytoin.

This list is not complete. Other drugs may affect tadalafil, including prescription and over-the-counter medicines, vitamins, and herbal products. Not all possible drug interactions are listed here.

Where can I get more information?

Your pharmacist can provide more information about tadalafil.

Remember, keep this and all other medicines out of the reach of children, never share your medicines with others, and use this medication only for the indication prescribed.

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